paper_id
uint32 | title
string | authors
list | cvf_url
string | pdf_url
string | supp_url
string | bibtex
string | abstract
large_string | arxiv_id
string | comment
string | github
string | project_page
string | space_ids
list | model_ids
list | dataset_ids
list | embedding
list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
400
|
HccePose(BF): Predicting Front & Back Surfaces to Construct Ultra-Dense 2D-3D Correspondences for Pose Estimation
|
[
"Yulin Wang",
"Mengting Hu",
"Hongli Li",
"Chen Luo"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_HccePoseBF_Predicting_Front__Back_Surfaces_to_Construct_Ultra-Dense_2D-3D_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_HccePoseBF_Predicting_Front__Back_Surfaces_to_Construct_Ultra-Dense_2D-3D_ICCV_2025_paper.pdf
| null |
@InProceedings{Wang_2025_ICCV,
author = {Wang, Yulin and Hu, Mengting and Li, Hongli and Luo, Chen},
title = {HccePose(BF): Predicting Front \& Back Surfaces to Construct Ultra-Dense 2D-3D Correspondences for Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {7166-7175}
}
|
In pose estimation for seen objects, a prevalent pipeline involves using neural networks to predict dense 3D coordinates of the object surface on 2D images, which are then used to establish dense 2D-3D correspondences. However, current methods primarily focus on more efficient encoding techniques to improve the precision of predicted 3D coordinates on the object's front surface, overlooking the potential benefits of incorporating the back surface and interior of the object. To better utilize the full surface and interior of the object, this study predicts 3D coordinates of both the object's front and back surfaces and densely samples 3D coordinates between them. This process creates ultra-dense 2D-3D correspondences, effectively enhancing pose estimation accuracy based on the Perspective-n-Point (PnP) algorithm. Additionally, we propose Hierarchical Continuous Coordinate Encoding (HCCE) to provide a more accurate and efficient representation of front and back surface coordinates. Experimental results show that, compared to existing state-of-the-art (SOTA) methods on the BOP website, the proposed approach outperforms across seven classic BOP core datasets.
|
2510.10177
| null | null | null |
[] |
[] |
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] |
401
|
Find Any Part in 3D
|
[
"Ziqi Ma",
"Yisong Yue",
"Georgia Gkioxari"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ma_Find_Any_Part_in_3D_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ma_Find_Any_Part_in_3D_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ma_Find_Any_Part_ICCV_2025_supplemental.pdf
|
@InProceedings{Ma_2025_ICCV,
author = {Ma, Ziqi and Yue, Yisong and Gkioxari, Georgia},
title = {Find Any Part in 3D},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {7818-7827}
}
|
Why don't we have foundation models in 3D yet? A key limitation is data scarcity. For 3D object part segmentation, existing datasets are small in size and lack diversity. We show that it is possible to break this data barrier by building a data engine powered by 2D foundation models. Our data engine automatically annotates any number of object parts: 1755x more unique part types than existing datasets combined. By training on our annotated data with a simple contrastive objective, we obtain an open-world model that generalizes to any part in any object based on any text query. Even when evaluated zero-shot, we outperform existing methods on the datasets they train on. We achieve 260% improvement in mIoU and boost speed by 6x to 300x. Our scaling analysis confirms that this generalization stems from the data scale, which underscores the impact of our data engine. Finally, to advance general-category open-world 3D part segmentation, we release a benchmark covering a wide range of objects and parts. Project website: https://ziqi-ma.github.io/find3dsite/
|
2411.13550
|
Project website: https://ziqi-ma.github.io/find3dsite/
| null | null |
[
"ziqima/Find3D"
] |
[
"ziqima/find3d-checkpt0"
] |
[
"ziqima/Objaverse-General-Find3D"
] |
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] |
402
|
CompleteMe: Reference-based Human Image Completion
|
[
"Yu-Ju Tsai",
"Brian Price",
"Qing Liu",
"Luis Figueroa",
"Daniil Pakhomov",
"Zhihong Ding",
"Scott Cohen",
"Ming-Hsuan Yang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Tsai_CompleteMe_Reference-based_Human_Image_Completion_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Tsai_CompleteMe_Reference-based_Human_Image_Completion_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Tsai_CompleteMe_Reference-based_Human_ICCV_2025_supplemental.pdf
|
@InProceedings{Tsai_2025_ICCV,
author = {Tsai, Yu-Ju and Price, Brian and Liu, Qing and Figueroa, Luis and Pakhomov, Daniil and Ding, Zhihong and Cohen, Scott and Yang, Ming-Hsuan},
title = {CompleteMe: Reference-based Human Image Completion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {18252-18261}
}
|
Recent methods for human image completion can reconstruct plausible body shapes but often fail to preserve unique details, such as specific clothing patterns or distinctive accessories, without explicit reference images. Even state-of-the-art reference-based inpainting approaches struggle to accurately capture and integrate fine-grained details from reference images. To address this limitation, we propose CompleteMe, a novel reference-based human image completion framework. CompleteMe employs a dual U-Net architecture combined with a Region-focused Attention (RFA) Block, which explicitly guides the model's attention toward relevant regions in reference images. This approach effectively captures fine details and ensures accurate semantic correspondence, significantly improving the fidelity and consistency of completed images. Additionally, we introduce a challenging benchmark specifically designed for evaluating reference-based human image completion tasks. Extensive experiments demonstrate that our proposed method achieves superior visual quality and semantic consistency compared to existing techniques.
|
2504.20042
|
Project page: https://liagm.github.io/CompleteMe/
| null | null |
[] |
[] |
[] |
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] |
403
|
MMOne: Representing Multiple Modalities in One Scene
|
[
"Zhifeng Gu",
"Bing Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Gu_MMOne_Representing_Multiple_Modalities_in_One_Scene_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Gu_MMOne_Representing_Multiple_Modalities_in_One_Scene_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Gu_MMOne_Representing_Multiple_ICCV_2025_supplemental.pdf
|
@InProceedings{Gu_2025_ICCV,
author = {Gu, Zhifeng and Wang, Bing},
title = {MMOne: Representing Multiple Modalities in One Scene},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1088-1098}
}
|
Humans perceive the world through multimodal cues to understand and interact with the environment. Learning a scene representation for multiple modalities enhances comprehension of the physical world. However, modality conflicts, arising from inherent distinctions among different modalities, present two critical challenges: property disparity and granularity disparity. To address these challenges, we propose a general framework, MMOne, to represent multiple modalities in one scene, which can be readily extended to additional modalities. Specifically, a modality modeling module with a novel modality indicator is proposed to capture the unique properties of each modality. Additionally, we design a multimodal decomposition mechanism to separate multi-modal Gaussians into single-modal Gaussians based on modality differences. We address the essential distinctions among modalities by disentangling multimodal information into shared and modality-specific components, resulting in a more compact and efficient multimodal scene representation. Extensive experiments demonstrate that our method consistently enhances the representation capability for each modality and is scalable to additional modalities. The code is available at https://github.com/Neal2020GitHub/MMOne.
|
2507.11129
|
Accepted to ICCV 2025
|
https://github.com/Neal2020GitHub/MMOne
| null |
[] |
[] |
[] |
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] |
404
|
VA-MoE: Variables-Adaptive Mixture of Experts for Incremental Weather Forecasting
|
[
"Hao Chen",
"Han Tao",
"Guo Song",
"Jie Zhang",
"Yonghan Dong",
"Yunlong Yu",
"Lei Bai"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_VA-MoE_Variables-Adaptive_Mixture_of_Experts_for_Incremental_Weather_Forecasting_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_VA-MoE_Variables-Adaptive_Mixture_of_Experts_for_Incremental_Weather_Forecasting_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_VA-MoE_Variables-Adaptive_Mixture_ICCV_2025_supplemental.pdf
|
@InProceedings{Chen_2025_ICCV,
author = {Chen, Hao and Tao, Han and Song, Guo and Zhang, Jie and Dong, Yonghan and Yu, Yunlong and Bai, Lei},
title = {VA-MoE: Variables-Adaptive Mixture of Experts for Incremental Weather Forecasting},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {7915-7924}
}
|
This paper presents Variables-Adaptive Mixture of Experts (VA-MoE), a novel framework for incremental weather forecasting that dynamically adapts to evolving spatiotemporal patterns in real-time data. Traditional weather prediction models often struggle with exorbitant computational expenditure and the need to continuously update forecasts as new observations arrive. VA-MoE addresses these challenges by leveraging a hybrid architecture of experts, where each expert specializes in capturing distinct sub-patterns of atmospheric variables (e.g., temperature, humidity, wind speed). Moreover, the proposed method employs a variable-adaptive gating mechanism to dynamically select and combine relevant experts based on the input context, enabling efficient knowledge distillation and parameter sharing. This design significantly reduces computational overhead while maintaining high forecast accuracy. Experiments on real-world ERA5 dataset demonstrate that VA-MoE performs comparable against state-of-the-art models in both short-term (e.g., 1-3 days) and long-term (e.g., 5 days) forecasting tasks, with only about 25% of trainable parameters and 50% of the initial training data. Code: https://github.com/chenhao-zju/VAMoE
| null | null | null | null |
[] |
[] |
[] |
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] |
405
|
2HandedAfforder: Learning Precise Actionable Bimanual Affordances from Human Videos
|
[
"Marvin Heidinger",
"Snehal Jauhri",
"Vignesh Prasad",
"Georgia Chalvatzaki"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Heidinger_2HandedAfforder_Learning_Precise_Actionable_Bimanual_Affordances_from_Human_Videos_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Heidinger_2HandedAfforder_Learning_Precise_Actionable_Bimanual_Affordances_from_Human_Videos_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Heidinger_2HandedAfforder_Learning_Precise_ICCV_2025_supplemental.zip
|
@InProceedings{Heidinger_2025_ICCV,
author = {Heidinger, Marvin and Jauhri, Snehal and Prasad, Vignesh and Chalvatzaki, Georgia},
title = {2HandedAfforder: Learning Precise Actionable Bimanual Affordances from Human Videos},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {14743-14753}
}
|
When interacting with objects, humans effectively reason about which regions of objects are viable for an intended action, i.e., the affordance regions of the object. They can also account for subtle differences in object regions based on the task to be performed and whether one or two hands need to be used. However, current vision-based affordance prediction methods often reduce the problem to naive object part segmentation. In this work, we propose a framework for extracting affordance data from human activity video datasets. Our extracted 2HANDS dataset contains precise object affordance region segmentations and affordance class-labels as narrations of the activity performed. The data also accounts for bimanual actions, i.e., two hands co-ordinating and interacting with one or more objects. We present a VLM-based affordance prediction model, 2HandedAfforder, trained on the dataset and demonstrate superior performance over baselines in affordance region segmentation for various activities. Finally, we show that our predicted affordance regions are actionable, i.e., can be used by an agent performing a task, through demonstration in robotic manipulation scenarios.
|
2503.09320
|
ICCV 2025
| null |
https://sites.google.com/view/2handedafforder
|
[] |
[] |
[
"sjauhri/2HANDS"
] |
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] |
406
|
GenDoP: Auto-regressive Camera Trajectory Generation as a Director of Photography
|
[
"Mengchen Zhang",
"Tong Wu",
"Jing Tan",
"Ziwei Liu",
"Gordon Wetzstein",
"Dahua Lin"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_GenDoP_Auto-regressive_Camera_Trajectory_Generation_as_a_Director_of_Photography_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_GenDoP_Auto-regressive_Camera_Trajectory_Generation_as_a_Director_of_Photography_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_GenDoP_Auto-regressive_Camera_ICCV_2025_supplemental.zip
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Mengchen and Wu, Tong and Tan, Jing and Liu, Ziwei and Wetzstein, Gordon and Lin, Dahua},
title = {GenDoP: Auto-regressive Camera Trajectory Generation as a Director of Photography},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {18229-18239}
}
|
Camera trajectory design plays a crucial role in video production, serving as a fundamental tool for conveying directorial intent and enhancing visual storytelling. In cinematography, Directors of Photography meticulously craft camera movements to achieve expressive and intentional framing. However, existing methods for camera trajectory generation remain limited: Traditional approaches rely on geometric optimization or handcrafted procedural systems, while recent learning-based methods often inherit structural biases or lack textual alignment, constraining creative synthesis. In this work, we introduce an auto-regressive model inspired by the expertise of Directors of Photography to generate artistic and expressive camera trajectories. We first introduce DataDoP, a large-scale multi-modal dataset containing 29K real-world shots with free-moving camera trajectories, depth maps, and detailed captions in specific movements, interaction with the scene, and directorial intent. Thanks to the comprehensive and diverse database, we further train an auto-regressive, decoder-only Transformer for high-quality, context-aware camera movement generation based on text guidance and RGBD inputs, named GenDoP. Extensive experiments demonstrate that compared to existing methods, GenDoP offers better controllability, finer-grained trajectory adjustments, and higher motion stability. We believe our approach establishes a new standard for learning-based cinematography, paving the way for future advancements in camera control and filmmaking. Our project website: https://kszpxxzmc.github.io/GenDoP.
|
2504.07083
| null | null |
https://kszpxxzmc.github.io/GenDoP/
|
[] |
[] |
[
"Dubhe-zmc/DataDoP"
] |
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] |
407
|
Latent Swap Joint Diffusion for 2D Long-Form Latent Generation
|
[
"Yusheng Dai",
"Chenxi Wang",
"Chang Li",
"Chen Wang",
"Kewei Li",
"Jun Du",
"Lei Sun",
"Jianqing Gao",
"Ruoyu Wang",
"Jiefeng Ma"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Dai_Latent_Swap_Joint_Diffusion_for_2D_Long-Form_Latent_Generation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Dai_Latent_Swap_Joint_Diffusion_for_2D_Long-Form_Latent_Generation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Dai_Latent_Swap_Joint_ICCV_2025_supplemental.pdf
|
@InProceedings{Dai_2025_ICCV,
author = {Dai, Yusheng and Wang, Chenxi and Li, Chang and Wang, Chen and Li, Kewei and Du, Jun and Sun, Lei and Gao, Jianqing and Wang, Ruoyu and Ma, Jiefeng},
title = {Latent Swap Joint Diffusion for 2D Long-Form Latent Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11006-11015}
}
|
This paper introduces Swap Forward (SaFa), a modality-agnostic and efficient method to generate seamless and coherent long spectrum and panorama using a latent swap joint diffusion process across multi-views. We first investigate spectrum aliasing problem in spectrum-based audio generation caused by existing joint diffusion methods. Through a comparative analysis of the VAE latent representation of spectra and RGB images, we identify that the failure arises from excessive suppression of high-frequency components due to the step-wise averaging operator. To address this issue, we propose Self-Loop Latent Swap, a frame-level bidirectional swap operator, applied to the overlapping region of adjacent views. Leveraging step-wise differentiated trajectories, this swap operator avoids spectrum distortion and adaptively enhances high-frequency components. Furthermore, to improve global cross-view consistency in non-overlapping regions, we introduce Reference-Guided Latent Swap, a unidirectional latent swap operator that provides a centralized reference trajectory to synchronize subview diffusions. By refining swap timing and intervals, we canachieve a balance between cross-view similarity and diversity in a feed-forward manner. Quantitative and qualitative experiments demonstrate that SaFa significantly outperforms existing joint diffusion methods and even training-based methods in audio generation using both U-Net and DiT models. It also adapts well to panorama generation, achieving comparable performance with a 2 xto 20 xspeedup. The project website is available at https://swapforward.github.io.
|
2502.05130
| null | null |
https://swapforward.github.io
|
[] |
[] |
[] |
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] |
408
|
Neural Inverse Rendering for High-Accuracy 3D Measurement of Moving Objects with Fewer Phase-Shifting Patterns
|
[
"Yuki Urakawa",
"Yoshihiro Watanabe"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Urakawa_Neural_Inverse_Rendering_for_High-Accuracy_3D_Measurement_of_Moving_Objects_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Urakawa_Neural_Inverse_Rendering_for_High-Accuracy_3D_Measurement_of_Moving_Objects_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Urakawa_Neural_Inverse_Rendering_ICCV_2025_supplemental.pdf
|
@InProceedings{Urakawa_2025_ICCV,
author = {Urakawa, Yuki and Watanabe, Yoshihiro},
title = {Neural Inverse Rendering for High-Accuracy 3D Measurement of Moving Objects with Fewer Phase-Shifting Patterns},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27692-27701}
}
|
Among structured-light methods, the phase-shifting approach enables high-resolution and high-accuracy measurements using a minimum of three patterns. However, its performance is significantly affected when dynamic and complex-shaped objects are measured, as motion artifacts and phase inconsistencies can degrade accuracy. In this study, we propose an enhanced phase-shifting method that incorporates neural inverse rendering to enable the 3D measurement of moving objects. To effectively capture object motion, we introduce a displacement field into the rendering model, which accurately represents positional changes and mitigates motion-induced distortions. Additionally, to achieve high-precision reconstruction with fewer phase-shifting patterns, we design a multiview-rendering framework that utilizes multiple cameras in conjunction with a single projector. Comparisons with state-of-the-art methods and various ablation studies demonstrated that our method accurately reconstructs the shapes of moving objects, even with a small number of patterns, using only simple, well-known phase-shifting patterns.
| null | null | null | null |
[] |
[] |
[] |
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] |
409
|
Embodied Navigation with Auxiliary Task of Action Description Prediction
|
[
"Haru Kondoh",
"Asako Kanezaki"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Kondoh_Embodied_Navigation_with_Auxiliary_Task_of_Action_Description_Prediction_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Kondoh_Embodied_Navigation_with_Auxiliary_Task_of_Action_Description_Prediction_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Kondoh_Embodied_Navigation_with_ICCV_2025_supplemental.zip
|
@InProceedings{Kondoh_2025_ICCV,
author = {Kondoh, Haru and Kanezaki, Asako},
title = {Embodied Navigation with Auxiliary Task of Action Description Prediction},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {7025-7036}
}
|
The field of multimodal robot navigation in indoor environments has garnered significant attention in recent years. However, as tasks and methods become more advanced, the action decision systems tend to become more complex and operate as black-boxes. For a reliable system, the ability to explain or describe its decisions is crucial; however, there tends to be a trade-off in that explainable systems cannot outperform non-explainable systems in terms of performance. In this paper, we propose incorporating the task of describing actions in language into the reinforcement learning of navigation as an auxiliary task. Existing studies have found it difficult to incorporate describing actions into reinforcement learning due to the absence of ground-truth data. We address this issue by leveraging knowledge distillation from pre-trained description generation models, such as vision-language models. We comprehensively evaluate our approach across various navigation tasks, demonstrating that it can describe actions while attaining high navigation performance. Furthermore, it achieves state-of-the-art performance in the particularly challenging multimodal navigation task of semantic audio-visual navigation.
| null | null | null | null |
[] |
[] |
[] |
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] |
410
|
Taming the Untamed: Graph-Based Knowledge Retrieval and Reasoning for MLLMs to Conquer the Unknown
|
[
"Bowen Wang",
"Zhouqiang Jiang",
"Yasuaki Susumu",
"Shotaro Miwa",
"Tianwei Chen",
"Yuta Nakashima"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Taming_the_Untamed_Graph-Based_Knowledge_Retrieval_and_Reasoning_for_MLLMs_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Taming_the_Untamed_Graph-Based_Knowledge_Retrieval_and_Reasoning_for_MLLMs_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_Taming_the_Untamed_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Bowen and Jiang, Zhouqiang and Susumu, Yasuaki and Miwa, Shotaro and Chen, Tianwei and Nakashima, Yuta},
title = {Taming the Untamed: Graph-Based Knowledge Retrieval and Reasoning for MLLMs to Conquer the Unknown},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4732-4742}
}
|
The real value of knowledge lies not just in its accumulation, but in its potential to be harnessed effectively to conquer the unknown. Although recent multimodal large language models (MLLMs) exhibit impressing multimodal capabilities, they often fail in rarely encountered domain-specific tasks due to limited relevant knowledge. To explore this, we adopt visual game cognition as a testbed and select "Monster Hunter: World" as the target to construct a multimodal knowledge graph (MH-MMKG), which incorporates multi-modalities and intricate entity relations. We also design a series of challenging queries based on MH-MMKG to evaluate the models' ability for complex knowledge retrieval and reasoning. Furthermore, we propose a multi-agent retriever that enables a model to autonomously search relevant knowledge without additional training. Experimental results show that our approach significantly enhances the performance of MLLMs, providing a new perspective on multimodal knowledge-augmented reasoning and laying a solid foundation for future research.
|
2506.17589
|
Aligned with ICCV 2025 camera-ready version
| null | null |
[] |
[] |
[] |
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] |
411
|
From Image to Video: An Empirical Study of Diffusion Representations
|
[
"Pedro Vélez",
"Luisa F. Polanía",
"Yi Yang",
"Chuhan Zhang",
"Rishabh Kabra",
"Anurag Arnab",
"Mehdi S. M. Sajjadi"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Velez_From_Image_to_Video_An_Empirical_Study_of_Diffusion_Representations_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Velez_From_Image_to_Video_An_Empirical_Study_of_Diffusion_Representations_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Velez_From_Image_to_ICCV_2025_supplemental.zip
|
@InProceedings{Velez_2025_ICCV,
author = {V\'elez, Pedro and Polan{\'\i}a, Luisa F. and Yang, Yi and Zhang, Chuhan and Kabra, Rishabh and Arnab, Anurag and Sajjadi, Mehdi S. M.},
title = {From Image to Video: An Empirical Study of Diffusion Representations},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {16948-16958}
}
|
Diffusion models have revolutionized generative modeling, enabling unprecedented realism in image and video synthesis.This success has sparked interest in leveraging their representations for visual understanding tasks. While recent works have explored this potential for image generation, the visual understanding capabilities of video diffusion models remain largely uncharted. To address this gap, we analyze the performance of latent image and video diffusion representations on various downstream tasks including image classification, action recognition, depth estimation, and tracking. For the most informative comparison, we utilize the same model architecture, WALT, across image and video generation objectives. Our results show that video generation pre-training consistently outperforms its image counterpart, though we find a striking range in the extent of this superiority. We further analyze features extracted from different layers and with varying noise levels, as well as the effect of model size and training budget on representation and generation quality. This work marks the first direct comparison of video and image diffusion objectives for visual understanding, offering insights into the role of temporal information in representation learning.
|
2502.07001
| null | null | null |
[] |
[] |
[] |
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] |
412
|
Diffusion Curriculum: Synthetic-to-Real Data Curriculum via Image-Guided Diffusion
|
[
"Yijun Liang",
"Shweta Bhardwaj",
"Tianyi Zhou"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liang_Diffusion_Curriculum_Synthetic-to-Real_Data_Curriculum_via_Image-Guided_Diffusion_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liang_Diffusion_Curriculum_Synthetic-to-Real_Data_Curriculum_via_Image-Guided_Diffusion_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liang_Diffusion_Curriculum_Synthetic-to-Real_ICCV_2025_supplemental.pdf
|
@InProceedings{Liang_2025_ICCV,
author = {Liang, Yijun and Bhardwaj, Shweta and Zhou, Tianyi},
title = {Diffusion Curriculum: Synthetic-to-Real Data Curriculum via Image-Guided Diffusion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1697-1707}
}
|
Low-quality or scarce data has posed significant challenges for training deep neural networks in practice. While classical data augmentation cannot contribute very different new data, diffusion models opens up a new door to build self-evolving AI by generating high-quality and diverse synthetic data through text-guided prompts. However, text-only guidance cannot control synthetic images' proximity to the original images, resulting in out-of-distribution data detrimental to the model performance. To overcome the limitation, we study image guidance to achieve a spectrum of interpolations between synthetic and real images. With stronger image guidance, the generated images are similar to the training data but hard to learn. While with weaker image guidance, the synthetic images will be easier for model but contribute to a larger distribution gap with the original data. The generated full spectrum of data enables us to build a novel "Diffusion CurricuLum (DisCL)". DisCL adjusts the image guidance level of image synthesis for each training stage: It identifies and focuses on hard samples for the model and assesses the most effective guidance level of synthetic images to improve hard data learning. We apply DisCL to two challenging tasks: long-tail (LT) classification and learning from low-quality data. It focuses on lower-guidance images of high-quality to learn prototypical features as a warm-up of learning higher-guidance images that might be weak on diversity or quality. Extensive experiments showcase a gain of 2.7% and 2.1% in OOD and ID macro-accuracy when applying DisCL to iWildCam dataset. On ImageNet-LT, DisCL improves the base model's tail-class accuracy from 4.4% to 23.64% and leads to a 4.02% improvement in all-class accuracy.
|
2410.13674
| null | null | null |
[] |
[] |
[] |
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] |
413
|
Dynamic-VLM: Simple Dynamic Visual Token Compression for VideoLLM
|
[
"Han Wang",
"Yuxiang Nie",
"Yongjie Ye",
"Yanjie Wang",
"Shuai Li",
"Haiyang Yu",
"Jinghui Lu",
"Can Huang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Dynamic-VLM_Simple_Dynamic_Visual_Token_Compression_for_VideoLLM_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Dynamic-VLM_Simple_Dynamic_Visual_Token_Compression_for_VideoLLM_ICCV_2025_paper.pdf
| null |
@InProceedings{Wang_2025_ICCV,
author = {Wang, Han and Nie, Yuxiang and Ye, Yongjie and Wang, Yanjie and Li, Shuai and Yu, Haiyang and Lu, Jinghui and Huang, Can},
title = {Dynamic-VLM: Simple Dynamic Visual Token Compression for VideoLLM},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {20812-20823}
}
|
The application of Large Vision-Language Models (LVLMs) for analyzing images and videos is an exciting and rapidly evolving field. In recent years, we've seen significant growth in high-quality image-text datasets for fine-tuning image understanding, but there is still a lack of comparable datasets for videos. Additionally, many VideoLLMs are extensions of single-image VLMs, which may not efficiently handle the complexities of longer videos. In this study, we introduce a large-scale synthetic dataset created from proprietary models, using carefully designed prompts to tackle a wide range of questions. We also explore a dynamic visual token compression architecture that strikes a balance between computational efficiency and performance. Our proposed Dynamic-VLM achieves state-of-the-art results across various video tasks and shows impressive generalization, setting new baselines in multi-image understanding. Notably, Dynamic-VLM delivers an absolute improvement of 2.7% over LLaVA-OneVision on VideoMME and 10.7% on MuirBench.
| null | null | null | null |
[] |
[] |
[] |
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] |
414
|
CCL-LGS: Contrastive Codebook Learning for 3D Language Gaussian Splatting
|
[
"Lei Tian",
"Xiaomin Li",
"Liqian Ma",
"Hao Yin",
"Zirui Zheng",
"Hefei Huang",
"Taiqing Li",
"Huchuan Lu",
"Xu Jia"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Tian_CCL-LGS_Contrastive_Codebook_Learning_for_3D_Language_Gaussian_Splatting_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Tian_CCL-LGS_Contrastive_Codebook_Learning_for_3D_Language_Gaussian_Splatting_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Tian_CCL-LGS_Contrastive_Codebook_ICCV_2025_supplemental.pdf
|
@InProceedings{Tian_2025_ICCV,
author = {Tian, Lei and Li, Xiaomin and Ma, Liqian and Yin, Hao and Zheng, Zirui and Huang, Hefei and Li, Taiqing and Lu, Huchuan and Jia, Xu},
title = {CCL-LGS: Contrastive Codebook Learning for 3D Language Gaussian Splatting},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9855-9864}
}
|
Recent advances in 3D reconstruction techniques and vision-language models have fueled significant progress in 3D semantic understanding, a capability critical to robotics, autonomous driving, and virtual/augmented reality. However, methods that rely on 2D priors are prone to a critical challenge: cross-view semantic inconsistencies induced by occlusion, image blur, and view-dependent variations. These inconsistencies, when propagated via projection supervision, deteriorate the quality of 3D Gaussian semantic fields and introduce artifacts in the rendered outputs. To mitigate this limitation, we propose CCL-LGS, a novel framework that enforces view-consistent semantic supervision by integrating multi-view semantic cues. Specifically, our approach first employs a zero-shot tracker to align a set of SAM-generated 2D masks and reliably identify their corresponding categories. Next, we utilize CLIP to extract robust semantic encodings across views. Finally, our Contrastive Codebook Learning (CCL) module distills discriminative semantic features by enforcing intra-class compactness and inter-class distinctiveness. In contrast to previous methods that directly apply CLIP to imperfect masks, our framework explicitly resolves semantic conflicts while preserving category discriminability. Extensive experiments demonstrate that CCL-LGS outperforms previous state-of-the-art methods. Our project page is available at https://epsilontl.github.io/CCL-LGS/.
| null | null | null | null |
[] |
[] |
[] |
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] |
415
|
TRACE: Learning 3D Gaussian Physical Dynamics from Multi-view Videos
|
[
"Jinxi Li",
"Ziyang Song",
"Bo Yang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_TRACE_Learning_3D_Gaussian_Physical_Dynamics_from_Multi-view_Videos_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_TRACE_Learning_3D_Gaussian_Physical_Dynamics_from_Multi-view_Videos_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_TRACE_Learning_3D_ICCV_2025_supplemental.zip
|
@InProceedings{Li_2025_ICCV,
author = {Li, Jinxi and Song, Ziyang and Yang, Bo},
title = {TRACE: Learning 3D Gaussian Physical Dynamics from Multi-view Videos},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {8820-8829}
}
|
In this paper, we aim to model 3D scene geometry, appearance, and physical information just from dynamic multi-view videos in the absence of any human labels. By leveraging physics-informed losses as soft constraints or integrating simple physics models into neural networks, existing works often fail to learn complex motion physics, or doing so requires additional labels such as object types or masks. In this paper, we propose a new framework named **TRACE** to model the motion physics of complex dynamic 3D scenes. The key novelty of our approach is that, by formulating each 3D point as a rigid particle with size and orientation in space, we choose to directly learn a translation rotation dynamics system for each particle, explicitly estimating a complete set of physical parameters to govern the particle's motion over time. Extensive experiments on three existing dynamic datasets and one newly created challenging synthetic datasets demonstrate the extraordinary performance of our method over baselines in the task of future frame extrapolation. A nice property of our framework is that multiple objects or parts can be easily segmented just by clustering the learned physical parameters. Our datasets and code will be released at https://github.com/.
|
2508.09811
|
ICCV 2025. Code and data are available at:
https://github.com/vLAR-group/TRACE
| null | null |
[] |
[] |
[] |
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] |
416
|
Two Losses, One Goal: Balancing Conflict Gradients for Semi-supervised Semantic Segmentation
|
[
"Rui Sun",
"Huayu Mai",
"Wangkai Li",
"Yujia Chen",
"Yuan Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Sun_Two_Losses_One_Goal_Balancing_Conflict_Gradients_for_Semi-supervised_Semantic_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Sun_Two_Losses_One_Goal_Balancing_Conflict_Gradients_for_Semi-supervised_Semantic_ICCV_2025_paper.pdf
| null |
@InProceedings{Sun_2025_ICCV,
author = {Sun, Rui and Mai, Huayu and Li, Wangkai and Chen, Yujia and Wang, Yuan},
title = {Two Losses, One Goal: Balancing Conflict Gradients for Semi-supervised Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {20357-20367}
}
|
Semi-supervised semantic segmentation has attracted considerable attention as it alleviates the need for extensive pixel-level annotations. However, existing methods often overlook the potential optimization conflict between supervised and unsupervised learning objectives, leading to suboptimal performance. In this paper, we identify this under-explored issue and propose a novel Pareto Optimization Strategy (POS) to tackle it. POS aims to find a descent gradient direction that benefits both learning objectives, thereby facilitating model training. By dynamically assigning weights to the gradients at each iteration based on the model's learning status, POS effectively reconciles the intrinsic tension between the two objectives. Furthermore, we analyze POS from the perspective of gradient descent in random batch sampling and propose the Magnitude Enhancement Operation (MEO) to further unleash its potential by considering both direction and magnitude during gradient integration. Extensive experiments on challenging benchmarks demonstrate that integrating POS into existing semi-supervised segmentation methods yields consistent improvements across different data splits and architectures (CNN, Transformer), showcasing its effectiveness.
| null | null | null | null |
[] |
[] |
[] |
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] |
417
|
FreeCus: Free Lunch Subject-driven Customization in Diffusion Transformers
|
[
"Yanbing Zhang",
"Zhe Wang",
"Qin Zhou",
"Mengping Yang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_FreeCus_Free_Lunch_Subject-driven_Customization_in_Diffusion_Transformers_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_FreeCus_Free_Lunch_Subject-driven_Customization_in_Diffusion_Transformers_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_FreeCus_Free_Lunch_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Yanbing and Wang, Zhe and Zhou, Qin and Yang, Mengping},
title = {FreeCus: Free Lunch Subject-driven Customization in Diffusion Transformers},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {15521-15531}
}
|
In light of recent breakthroughs in text-to-image (T2I) generation, particularly with diffusion transformers (DiT), subject-driven technologies are increasingly being employed for high-fidelity customized production that preserves subject identity from reference inputs, enabling thrilling design workflows and engaging entertainment. Existing alternatives typically require either per-subject optimization via trainable text embeddings or training specialized encoders for subject feature extraction on large-scale datasets. Such dependencies on training procedures fundamentally constrain their practical applications. More importantly, current methodologies fail to fully leverage the inherent zero-shot potential of modern diffusion transformers (e.g., the Flux series) for authentic subject-driven synthesis. To bridge this gap, we propose FreeCus, a genuinely training-free framework that activates DiT's capabilities through three key innovations: 1) We introduce a pivotal attention sharing mechanism that captures the subject's layout integrity while preserving crucial editing flexibility. 2) Through a straightforward analysis of DiT's dynamic shifting, we propose an upgraded variant that significantly improves fine-grained feature extraction. 3) We further integrate advanced Multimodal Large Language Models (MLLMs) to enrich cross-modal semantic representations. Extensive experiments reflect that our method successfully unlocks DiT's zero-shot ability for consistent subject synthesis across diverse contexts, achieving state-of-the-art or comparable results compared to approaches that require additional training. Notably, our framework demonstrates seamless compatibility with existing inpainting pipelines and control modules, facilitating more compelling experiences. Our code is available at: https://github.com/Monalissaa/FreeCus.
|
2507.15249
|
Accepted by ICCV 2025
|
https://github.com/Monalissaa/FreeCus
| null |
[] |
[] |
[] |
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] |
418
|
Trokens: Semantic-Aware Relational Trajectory Tokens for Few-Shot Action Recognition
|
[
"Pulkit Kumar",
"Shuaiyi Huang",
"Matthew Walmer",
"Sai Saketh Rambhatla",
"Abhinav Shrivastava"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Kumar_Trokens_Semantic-Aware_Relational_Trajectory_Tokens_for_Few-Shot_Action_Recognition_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Kumar_Trokens_Semantic-Aware_Relational_Trajectory_Tokens_for_Few-Shot_Action_Recognition_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Kumar_Trokens_Semantic-Aware_Relational_ICCV_2025_supplemental.pdf
|
@InProceedings{Kumar_2025_ICCV,
author = {Kumar, Pulkit and Huang, Shuaiyi and Walmer, Matthew and Rambhatla, Sai Saketh and Shrivastava, Abhinav},
title = {Trokens: Semantic-Aware Relational Trajectory Tokens for Few-Shot Action Recognition},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13544-13556}
}
|
Video understanding requires effective modeling of both motion and appearance information, particularly for few-shot action recognition. While recent advances in point tracking have been shown to improve few-shot action recognition, two fundamental challenges persist: selecting informative points to track and effectively modeling their motion patterns. We present Trokens, a novel approach that transforms trajectory points into semantic-aware relational tokens for action recognition. First, we introduce a semantic-aware sampling strategy to adaptively distribute tracking points based on object scale and semantic relevance. Second, we develop a motion modeling framework that captures both intra-trajectory dynamics through the Histogram of Oriented Displacements (HoD) and inter-trajectory relationships to model complex action patterns. Our approach effectively combines these trajectory tokens with semantic features to enhance appearance features with motion information, achieving state-of-the-art performance across six diverse few-shot action recognition benchmarks: Something-Something-V2 (both full and small splits), Kinetics, UCF101, HMDB51, and FineGym.
|
2508.03695
|
Accepted at ICCV 2025; First two authors contributed equally
| null | null |
[] |
[] |
[
"pulkitkumar95/trokens_pt_data"
] |
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] |
419
|
MotionStreamer: Streaming Motion Generation via Diffusion-based Autoregressive Model in Causal Latent Space
|
[
"Lixing Xiao",
"Shunlin Lu",
"Huaijin Pi",
"Ke Fan",
"Liang Pan",
"Yueer Zhou",
"Ziyong Feng",
"Xiaowei Zhou",
"Sida Peng",
"Jingbo Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xiao_MotionStreamer_Streaming_Motion_Generation_via_Diffusion-based_Autoregressive_Model_in_Causal_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xiao_MotionStreamer_Streaming_Motion_Generation_via_Diffusion-based_Autoregressive_Model_in_Causal_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xiao_MotionStreamer_Streaming_Motion_ICCV_2025_supplemental.pdf
|
@InProceedings{Xiao_2025_ICCV,
author = {Xiao, Lixing and Lu, Shunlin and Pi, Huaijin and Fan, Ke and Pan, Liang and Zhou, Yueer and Feng, Ziyong and Zhou, Xiaowei and Peng, Sida and Wang, Jingbo},
title = {MotionStreamer: Streaming Motion Generation via Diffusion-based Autoregressive Model in Causal Latent Space},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10086-10096}
}
|
This paper addresses the challenge of text-conditioned streaming motion generation, which requires us to predict the next-step human pose based on variable-length historical motions and incoming texts. Existing methods struggle to achieve streaming motion generation, e.g., diffusion models are constrained by pre-defined motion lengths, while GPT-based methods suffer from delayed response and error accumulation problem due to discretized non-causal tokenization. To solve these problems, we propose MotionStreamer, a novel framework that incorporates a continuous causal latent space into a probabilistic autoregressive model. The continuous latents mitigate information loss caused by discretization and effectively reduce error accumulation during long-term autoregressive generation. In addition, by establishing temporal causal dependencies between current and historical motion latents, our model fully utilizes the available information to achieve accurate online motion decoding. Experiments show that our method outperforms existing approaches while offering more applications, including multi-round generation, long-term generation, and dynamic motion composition. Project Page: https://zju3dv.github.io/MotionStreamer/
|
2503.15451
|
ICCV 2025. Project Page: https://zju3dv.github.io/MotionStreamer/
| null | null |
[] |
[
"lxxiao/MotionStreamer"
] |
[
"InternRobotics/MotionMillion"
] |
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] |
420
|
RS-vHeat: Heat Conduction Guided Efficient Remote Sensing Foundation Model
|
[
"Huiyang Hu",
"Peijin Wang",
"Hanbo Bi",
"Boyuan Tong",
"Zhaozhi Wang",
"Wenhui Diao",
"Hao Chang",
"Yingchao Feng",
"Ziqi Zhang",
"Yaowei Wang",
"Qixiang Ye",
"Kun Fu",
"Xian Sun"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Hu_RS-vHeat_Heat_Conduction_Guided_Efficient_Remote_Sensing_Foundation_Model_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Hu_RS-vHeat_Heat_Conduction_Guided_Efficient_Remote_Sensing_Foundation_Model_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Hu_RS-vHeat_Heat_Conduction_ICCV_2025_supplemental.pdf
|
@InProceedings{Hu_2025_ICCV,
author = {Hu, Huiyang and Wang, Peijin and Bi, Hanbo and Tong, Boyuan and Wang, Zhaozhi and Diao, Wenhui and Chang, Hao and Feng, Yingchao and Zhang, Ziqi and Wang, Yaowei and Ye, Qixiang and Fu, Kun and Sun, Xian},
title = {RS-vHeat: Heat Conduction Guided Efficient Remote Sensing Foundation Model},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9876-9887}
}
|
Remote sensing foundation models largely break away from the traditional paradigm of designing task-specific models, offering greater scalability across multiple tasks. However, they face challenges such as low computational efficiency and limited interpretability, especially when dealing with large-scale remote sensing images. To overcome these, we draw inspiration from heat conduction, a physical process modeling local heat diffusion. Building on this idea, we are the first to explore the potential of using the parallel computing model of heat conduction to simulate the local region correlations in high-resolution remote sensing images, and introduce RS-vHeat, an efficient multi-modal remote sensing foundation model. Specifically, RS-vHeat 1) applies the Heat Conduction Operator (HCO) with a complexity of O(N^ 1.5 ) and a global receptive field, reducing computational overhead while capturing remote sensing object structure information to guide heat diffusion; 2) learns the frequency distribution representations of various scenes through a self-supervised strategy based on frequency domain hierarchical masking and multi-domain reconstruction; 3) significantly improves efficiency and performance over state-of-the-art techniques across 4 tasks and 10 datasets. Compared to attention-based remote sensing foundation models, we reduce memory usage by 84%, FLOPs by 24% and improves throughput by 2.7 times. The code will be made publicly available.
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[] |
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] |
421
|
EA-Vit: Efficient Adaptation for Elastic Vision Transformer
|
[
"Chen Zhu",
"Wangbo Zhao",
"Huiwen Zhang",
"Yuhao Zhou",
"Weidong Tang",
"Shuo Wang",
"Zhihang Yuan",
"Yuzhang Shang",
"Xiaojiang Peng",
"Kai Wang",
"Dawei Yang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhu_EA-Vit_Efficient_Adaptation_for_Elastic_Vision_Transformer_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhu_EA-Vit_Efficient_Adaptation_for_Elastic_Vision_Transformer_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhu_EA-Vit_Efficient_Adaptation_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhu_2025_ICCV,
author = {Zhu, Chen and Zhao, Wangbo and Zhang, Huiwen and Zhou, Yuhao and Tang, Weidong and Wang, Shuo and Yuan, Zhihang and Shang, Yuzhang and Peng, Xiaojiang and Wang, Kai and Yang, Dawei},
title = {EA-Vit: Efficient Adaptation for Elastic Vision Transformer},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1038-1047}
}
|
Vision Transformers (ViTs) have emerged as a foundational model in computer vision, excelling in generalization and adaptation to downstream tasks. However, deploying ViTs to support diverse resource constraints typically requires retraining multiple, size-specific ViTs, which is both time-consuming and energy-intensive. To address this issue, we propose an efficient ViT adaptation framework that enables a single adaptation process to generate multiple models of varying sizes for deployment on platforms with various resource constraints. Our approach comprises two stages. In the first stage, we enhance a pre-trained ViT with a nested elastic architecture that enables structural flexibility across MLP expansion ratio, number of attention heads, embedding dimension, and network depth. To preserve pre-trained knowledge and ensure stable adaptation, we adopt a curriculum-based training strategy that progressively increases elasticity. In the second stage, we design a lightweight router to select submodels according to computational budgets and downstream task demands. Initialized with Pareto-optimal configurations derived via a customized NSGA-II algorithm, the router is then jointly optimized with the backbone. Extensive experiments on multiple benchmarks demonstrate the effectiveness and versatility of EA-ViT. The code is available at https://github.com/zcxcf/EA-ViT.
| null | null | null | null |
[] |
[] |
[] |
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] |
422
|
Aether: Geometric-Aware Unified World Modeling
|
[
"Haoyi Zhu",
"Yifan Wang",
"Jianjun Zhou",
"Wenzheng Chang",
"Yang Zhou",
"Zizun Li",
"Junyi Chen",
"Chunhua Shen",
"Jiangmiao Pang",
"Tong He"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhu_Aether_Geometric-Aware_Unified_World_Modeling_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhu_Aether_Geometric-Aware_Unified_World_Modeling_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhu_Aether_Geometric-Aware_Unified_ICCV_2025_supplemental.zip
|
@InProceedings{Zhu_2025_ICCV,
author = {Zhu, Haoyi and Wang, Yifan and Zhou, Jianjun and Chang, Wenzheng and Zhou, Yang and Li, Zizun and Chen, Junyi and Shen, Chunhua and Pang, Jiangmiao and He, Tong},
title = {Aether: Geometric-Aware Unified World Modeling},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {8535-8546}
}
|
The integration of geometric reconstruction and generative modeling remains a critical challenge in developing AI systems capable of human-like spatial reasoning. This paper proposes Aether, a unified framework that enables geometry-aware reasoning in world models by jointly optimizing three core capabilities: (1) 4D dynamic reconstruction, (2) action-conditioned video prediction, and (3) goal-conditioned visual planning. Through task-interleaved feature learning, Aether achieves synergistic knowledge sharing across reconstruction, prediction, and planning objectives. Building upon video generation models, our framework demonstrates zero-shot synthetic-to-real generalization despite never observing real-world data during training. Furthermore, our approach achieves zero-shot generalization in both action following and reconstruction tasks, thanks to its intrinsic geometric modeling. Notably, even without real-world data, its reconstruction performance is comparable with or even better than that of domain-specific models. Additionally, Aether employs camera trajectories as geometry-informed action spaces, enabling effective action-conditioned prediction and visual planning. We hope our work inspires the community to explore new frontiers in physically-reasonable world modeling and its applications.
|
2503.18945
|
Project Page: https://aether-world.github.io/
| null | null |
[
"AmberHeart/AetherV1"
] |
[
"AetherWorldModel/AetherV1"
] |
[] |
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] |
423
|
ArtEditor: Learning Customized Instructional Image Editor from Few-Shot Examples
|
[
"Shijie Huang",
"Yiren Song",
"Yuxuan Zhang",
"Hailong Guo",
"Xueyin Wang",
"Jiaming Liu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Huang_ArtEditor_Learning_Customized_Instructional_Image_Editor_from_Few-Shot_Examples_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Huang_ArtEditor_Learning_Customized_Instructional_Image_Editor_from_Few-Shot_Examples_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Huang_ArtEditor_Learning_Customized_ICCV_2025_supplemental.pdf
|
@InProceedings{Huang_2025_ICCV,
author = {Huang, Shijie and Song, Yiren and Zhang, Yuxuan and Guo, Hailong and Wang, Xueyin and Liu, Jiaming},
title = {ArtEditor: Learning Customized Instructional Image Editor from Few-Shot Examples},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {17651-17662}
}
|
We introduce ArtEditor, a novel framework for instruction-based image editing that learns unique editing styles from few-shot examples. While image editing has seen significant advancements, customized instructional editing remains underexplored. Existing methods often rely on complex, multi-stage pipelines that are difficult to adapt to specific styles. Additionally, this domain lacks a standardized benchmark, making it challenging to evaluate progress. To address these issues, we propose ArtEditor, a two-stage training framework. In the first stage, we train ArtEditor-Base, a general-purpose image editing model, on large-scale datasets to build a strong foundational capability. In the second stage, we fine-tune this model using ArtEditor-LoRA, a lightweight adaptation module, on a small dataset of before-and-after image pairs. This approach enables the model to efficiently learn distinct editing styles and techniques with minimal data. To enhance the performance of a pre-trained Diffusion Transformer (DiT) model, we introduce two key innovations: position encoding cloning and a noise-free conditioning paradigm. These techniques ensure stable and coherent edits, even when adapting to new styles. To support research in this area, we contribute the DoodleArt dataset, the first benchmark specifically designed for customized image editing. DoodleArt features six high-quality artistic styles created by professional artists and designers, providing a valuable resource for evaluating and advancing future work. Extensive experiments demonstrate that ArtEditor achieves superior performance and robustness in customized image editing. Our framework opens new possibilities for artistic creation, offering artists intuitive and flexible tools to bring their visions to life.
| null | null | null | null |
[] |
[] |
[] |
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424
|
LiON-LoRA: Rethinking LoRA Fusion to Unify Controllable Spatial and Temporal Generation for Video Diffusion
|
[
"Yisu Zhang",
"Chenjie Cao",
"Chaohui Yu",
"Jianke Zhu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_LiON-LoRA_Rethinking_LoRA_Fusion_to_Unify_Controllable_Spatial_and_Temporal_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_LiON-LoRA_Rethinking_LoRA_Fusion_to_Unify_Controllable_Spatial_and_Temporal_ICCV_2025_paper.pdf
| null |
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Yisu and Cao, Chenjie and Yu, Chaohui and Zhu, Jianke},
title = {LiON-LoRA: Rethinking LoRA Fusion to Unify Controllable Spatial and Temporal Generation for Video Diffusion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {14569-14579}
}
|
Video Diffusion Models (VDMs) have demonstrated remarkable capabilities in synthesizing realistic videos by learning from large-scale data. Although vanilla Low-Rank Adaptation (LoRA) can learn specific spatial or temporal movement to driven VDMs with constrained data, achieving precise control over both camera trajectories and object motion remains challenging due to the unstable fusion and non-linear scalability. To address these issues, we propose LiON-LoRA, a novel framework that rethinks LoRA fusion through three core principles: Linear scalability, Orthogonality, and Norm consistency. First, we analyze the orthogonality of LoRA features in shallow VDM layers, enabling decoupled low-level controllability. Second, norm consistency is enforced across layers to stabilize fusion during complex camera motion combinations. Third, a controllable token is integrated into the diffusion transformer (DiT) to linearly adjust motion amplitudes for both cameras and objects with a modified self-attention mechanism to ensure decoupled control. Additionally, we extend LiON-LoRA to temporal generation by leveraging static-camera videos, unifying spatial and temporal controllability. Experiments demonstrate that LiON-LoRA outperforms state-of-the-art methods in trajectory control accuracy and motion strength adjustment, achieving superior generalization with minimal training data.
| null | null | null | null |
[] |
[] |
[] |
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] |
425
|
VLIPP: Towards Physically Plausible Video Generation with Vision and Language Informed Physical Prior
|
[
"Xindi Yang",
"Baolu Li",
"Yiming Zhang",
"Zhenfei Yin",
"Lei Bai",
"Liqian Ma",
"Zhiyong Wang",
"Jianfei Cai",
"Tien-Tsin Wong",
"Huchuan Lu",
"Xu Jia"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yang_VLIPP_Towards_Physically_Plausible_Video_Generation_with_Vision_and_Language_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yang_VLIPP_Towards_Physically_Plausible_Video_Generation_with_Vision_and_Language_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yang_VLIPP_Towards_Physically_ICCV_2025_supplemental.zip
|
@InProceedings{Yang_2025_ICCV,
author = {Yang, Xindi and Li, Baolu and Zhang, Yiming and Yin, Zhenfei and Bai, Lei and Ma, Liqian and Wang, Zhiyong and Cai, Jianfei and Wong, Tien-Tsin and Lu, Huchuan and Jia, Xu},
title = {VLIPP: Towards Physically Plausible Video Generation with Vision and Language Informed Physical Prior},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {12360-12370}
}
|
Video diffusion models (VDMs) have advanced significantly in recent years, enabling the generation of highly realistic videos and drawing the attention of the community in their potential as world simulators. However, despite their capabilities, VDMs often fail to produce physically plausible videos due to an inherent lack of understanding of physics, resulting in incorrect dynamics and event sequences. To address this limitation, we propose a novel two-stage image-to-video generation framework that explicitly incorporates physics with vision and language informed physical prior. In the first stage, we employ a Vision Language Model (VLM) as a coarse-grained motion planner, integrating chain-of-thought and physics-aware reasoning to predict a rough motion trajectories/changes that approximate real-world physical dynamics while ensuring the inter-frame consistency. In the second stage, we use the predicted motion trajectories/changes to guide the video generation of a VDM. As the predicted motion trajectories/changes are rough, noise is added during inference to provide freedom to the VDM in generating motion with more fine details. Extensive experimental results demonstrate that our framework can produce physically plausible motion, and comparative evaluations highlight the notable superiority of our approach over existing methods. More video results and code are available on our Project Page: https://madaoer.github.io/projects/physically_plausible_video_generation/.
|
2503.23368
|
18 pages, 11 figures
| null | null |
[] |
[] |
[] |
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] |
426
|
SAM4D: Segment Anything in Camera and LiDAR Streams
|
[
"Jianyun Xu",
"Song Wang",
"Ziqian Ni",
"Chunyong Hu",
"Sheng Yang",
"Jianke Zhu",
"Qiang Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xu_SAM4D_Segment_Anything_in_Camera_and_LiDAR_Streams_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xu_SAM4D_Segment_Anything_in_Camera_and_LiDAR_Streams_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xu_SAM4D_Segment_Anything_ICCV_2025_supplemental.zip
|
@InProceedings{Xu_2025_ICCV,
author = {Xu, Jianyun and Wang, Song and Ni, Ziqian and Hu, Chunyong and Yang, Sheng and Zhu, Jianke and Li, Qiang},
title = {SAM4D: Segment Anything in Camera and LiDAR Streams},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {28535-28545}
}
|
We present SAM4D, a multi-modal and temporal foundation model designed for promptable segmentation across camera and LiDAR streams. Unified Multi-modal Positional Encoding (UMPE) is introduced to align camera and LiDAR features in a shared 3D space, enabling seamless cross-modal prompting and interaction. Additionally, we propose Motion-aware Cross-modal Memory Attention (MCMA), which leverages ego-motion compensation to enhance temporal consistency and long-horizon feature retrieval, ensuring robust segmentation across dynamically changing autonomous driving scenes. To avoid annotation bottlenecks, we develop a multi-modal automated data engine that synergizes VFM-driven video masklets, spatiotemporal 4D reconstruction, and cross-modal masklet fusion. This framework generates camera-LiDAR aligned pseudo-labels at a speed orders of magnitude faster than human annotation while preserving VFM-derived semantic fidelity in point cloud representations. We conduct extensive experiments on the constructed Waymo-4DSeg, which demonstrate the powerful cross-modal segmentation ability and great potential in data annotation of proposed SAM4D.
|
2506.21547
|
Accepted by ICCV2025, Project Page: https://SAM4D-Project.github.io
| null |
https://sam4d-project.github.io
|
[] |
[] |
[] |
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] |
427
|
DreamRelation: Relation-Centric Video Customization
|
[
"Yujie Wei",
"Shiwei Zhang",
"Hangjie Yuan",
"Biao Gong",
"Longxiang Tang",
"Xiang Wang",
"Haonan Qiu",
"Hengjia Li",
"Shuai Tan",
"Yingya Zhang",
"Hongming Shan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wei_DreamRelation_Relation-Centric_Video_Customization_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wei_DreamRelation_Relation-Centric_Video_Customization_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wei_DreamRelation_Relation-Centric_Video_ICCV_2025_supplemental.zip
|
@InProceedings{Wei_2025_ICCV,
author = {Wei, Yujie and Zhang, Shiwei and Yuan, Hangjie and Gong, Biao and Tang, Longxiang and Wang, Xiang and Qiu, Haonan and Li, Hengjia and Tan, Shuai and Zhang, Yingya and Shan, Hongming},
title = {DreamRelation: Relation-Centric Video Customization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {12381-12393}
}
|
Relational video customization refers to the creation of personalized videos that depict user-specified relations between two subjects, a crucial task for comprehending real-world visual content. While existing methods can personalize subject appearances and motions, they still struggle with complex relational video customization, where precise relational modeling and high generalization across subject categories are essential. The primary challenge arises from the intricate spatial arrangements, layout variations, and nuanced temporal dynamics inherent in relations; consequently, current models tend to overemphasize irrelevant visual details rather than capturing meaningful interactions. To address these challenges, we propose DreamRelation, a novel approach that personalizes relations through a small set of exemplar videos, leveraging two key components: Relational Decoupling Learning and Relational Dynamics Enhancement. First, in Relational Decoupling Learning, we disentangle relations from subject appearances using relation LoRA triplet and hybrid mask training strategy, ensuring better generalization across diverse relationships. Furthermore, we determine the optimal design of relation LoRA triplet by analyzing the distinct roles of the query, key, and value features within MM-DiT's attention mechanism, making DreamRelation the first relational video generation framework with explainable components. Second, in Relational Dynamics Enhancement, we introduce space-time relational contrastive loss, which prioritizes relational dynamics while minimizing the reliance on detailed subject appearances. Extensive experiments demonstrate that DreamRelation outperforms state-of-the-art methods in relational video customization. Project page at: https://dreamrelation.github.io/.
|
2503.07602
|
Project Page: https://dreamrelation.github.io
| null | null |
[] |
[] |
[] |
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] |
428
|
DropletVideo: A Dataset and Approach to Explore Integral Spatio-Temporal Consistent Video Generation
|
[
"Runze Zhang",
"Guoguang Du",
"Xiaochuan Li",
"Qi Jia",
"Liang Jin",
"Lu Liu",
"Jingjing Wang",
"Cong Xu",
"Zhenhua Guo",
"Yaqian Zhao",
"Xiaoli Gong",
"Rengang Li",
"Baoyu Fan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_DropletVideo_A_Dataset_and_Approach_to_Explore_Integral_Spatio-Temporal_Consistent_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_DropletVideo_A_Dataset_and_Approach_to_Explore_Integral_Spatio-Temporal_Consistent_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_DropletVideo_A_Dataset_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Runze and Du, Guoguang and Li, Xiaochuan and Jia, Qi and Jin, Liang and Liu, Lu and Wang, Jingjing and Xu, Cong and Guo, Zhenhua and Zhao, Yaqian and Gong, Xiaoli and Li, Rengang and Fan, Baoyu},
title = {DropletVideo: A Dataset and Approach to Explore Integral Spatio-Temporal Consistent Video Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {15583-15593}
}
|
Spatio-temporal consistency is a critical topic in video generation. A qualified generated video segment must ensure plot plausibility and coherence while maintaining visual consistency of objects and scenes across varying viewpoints. Prior research, especially in open-source projects, primarily focuses on either temporal or spatial consistency, or their basic combination, such as appending a camera-movement description after a prompt without constraining its outcomes. However, camera movement may introduce new objects to the scene or eliminate existing ones, thereby affecting the preceding narrative. Especially in videos with numerous camera movements, the interplay between multiple plots becomes increasingly complex. This paper introduces and examines integral spatio-temporal consistency, considering the synergy between plot progression and camera techniques, and the long-term impact of prior content on subsequent generation. Our research encompasses dataset construction through to model development. Initially, we constructed DropletVideo-10M, which comprises 10 million videos that feature dynamic camera motion and object actions. With an average length of 206 words, the captions offer detailed accounts of camera movements. Following this, we developed the DropletVideo model, which excels in preserving spatio-temporal coherence during video generation. The work has been open-sourced: https://dropletx.github.io/.
|
2503.06053
| null | null | null |
[] |
[
"DropletX/DropletVideo-5B"
] |
[
"DropletX/DropletVideo-10M"
] |
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] |
429
|
TAB: Transformer Attention Bottlenecks enable User Intervention and Debugging in Vision-Language Models
|
[
"Pooyan Rahmanzadehgervi",
"Hung Huy Nguyen",
"Rosanne Liu",
"Long Mai",
"Anh Totti Nguyen"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Rahmanzadehgervi_TAB_Transformer_Attention_Bottlenecks_enable_User_Intervention_and_Debugging_in_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Rahmanzadehgervi_TAB_Transformer_Attention_Bottlenecks_enable_User_Intervention_and_Debugging_in_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Rahmanzadehgervi_TAB_Transformer_Attention_ICCV_2025_supplemental.pdf
|
@InProceedings{Rahmanzadehgervi_2025_ICCV,
author = {Rahmanzadehgervi, Pooyan and Nguyen, Hung Huy and Liu, Rosanne and Mai, Long and Nguyen, Anh Totti},
title = {TAB: Transformer Attention Bottlenecks enable User Intervention and Debugging in Vision-Language Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {22551-22562}
}
|
Multi-head self-attention (MHSA) is a key component of Transformers, a widely popular architecture in both language and vision. Multiple heads intuitively enable different parallel processes over the same input. Yet, they also obscure the attribution of each input patch to the output of a model. We propose a novel 1-head Transformer Attention Bottleneck (TAB) layer, inserted after the traditional MHSA architecture, to serve as an attention bottleneck for interpretability and intervention. Unlike standard self-attention, TAB constrains the total attention over all patches to \in [0, 1]. That is, when the total attention is 0, no visual information is propagated further into the network, and the vision-language model (VLM) would default to a generic, image-independent response. To demonstrate the advantages of TAB, we train VLMs with TAB to perform image-difference captioning. Over three datasets, our models perform similarly to baseline VLMs in captioning but the bottleneck is superior in localizing changes and in identifying when no changes occur. TAB is the first architecture to enable users to debug by editing attention, which often produces expected outputs by VLMs.
|
2412.18675
| null | null | null |
[] |
[] |
[] |
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430
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Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral Constraints
|
[
"Guanjie Chen",
"Xinyu Zhao",
"Yucheng Zhou",
"Xiaoye Qu",
"Tianlong Chen",
"Yu Cheng"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_Towards_Stabilized_and_Efficient_Diffusion_Transformers_through_Long-Skip-Connections_with_Spectral_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_Towards_Stabilized_and_Efficient_Diffusion_Transformers_through_Long-Skip-Connections_with_Spectral_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_Towards_Stabilized_and_ICCV_2025_supplemental.pdf
|
@InProceedings{Chen_2025_ICCV,
author = {Chen, Guanjie and Zhao, Xinyu and Zhou, Yucheng and Qu, Xiaoye and Chen, Tianlong and Cheng, Yu},
title = {Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral Constraints},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {17708-17718}
}
|
Diffusion Transformers (DiT) have emerged as a powerful architecture for image and video generation, offering superior quality and scalability. However, their practical application suffers from inherent dynamic feature instability, leading to error amplification during cached inference. Through systematic analysis, we identify the absence of long-range feature preservation mechanisms as the root cause of unstable feature propagation and perturbation sensitivity. To this end, we propose Skip-DiT, a novel DiT variant enhanced with Long-Skip-Connections (LSCs) - the key efficiency component in U-Nets. Theoretical spectral norm and visualization analysis demonstrate how LSCs stabilize feature dynamics. Skip-DiT architecture and its stabilized dynamic feature enable an efficient statical caching mechanism that reuses deep features across timesteps while updating shallow components. Extensive experiments across image and video generation tasks demonstrate that Skip-DiT achieves: (1) **4.4x** training acceleration and faster convergence, (2) **1.5-2x** inference acceleration without quality loss and high fidelity to original output, outperforming existing DiT caching methods across various quantitative metrics. Our findings establish long-skip connections as critical architectural components for training stable and efficient diffusion transformers. Codes are provided in the anonymous URL https://anonymous.4open.science/r/Skip-DiT-72B7/.
|
2411.17616
|
17 pages, 8 figures
|
https://github.com/OpenSparseLLMs/Skip-DiT
| null |
[] |
[
"GuanjieChen/Skip-DiT"
] |
[] |
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] |
431
|
Feature Extraction and Representation of Pre-training Point Cloud Based on Diffusion Models
|
[
"Chang Qiu",
"Feipeng Da",
"Zilei Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Qiu_Feature_Extraction_and_Representation_of_Pre-training_Point_Cloud_Based_on_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Qiu_Feature_Extraction_and_Representation_of_Pre-training_Point_Cloud_Based_on_ICCV_2025_paper.pdf
| null |
@InProceedings{Qiu_2025_ICCV,
author = {Qiu, Chang and Da, Feipeng and Zhang, Zilei},
title = {Feature Extraction and Representation of Pre-training Point Cloud Based on Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26559-26568}
}
|
The pretrain-finetune paradigm of pre-training a model on large amounts of image and text data and then fine-tuning the model for a specific task has led to significant progress in many 2D image and natural language processing tasks.Similarly, the use of pre-training methods in point cloud data can also enhance the working performance and generalization ability of the model.Therefore, in this paper, we propose a pre-training framework based on a diffusion model called PreDifPoint. It is able to accomplish the pre-training of the model's backbone network through a diffusion process of gradual denoising. We aggregate the potential features extracted from the backbone network, input them as conditions into the subsequent diffusion model, and direct the point-to-point mapping relationship of the noisy point clouds at neighboring time steps, so as to generate high-quality point clouds and at the same time better perform various downstream tasks of the point clouds.We also introduce a bi-directional covariate attention (DXCA-Attention) mechanism for capturing complex feature interactions, fusing local and global features, and improving the detail recovery of point clouds.In addition, we propose a density-adaptive sampling strategy, which can help the model dynamically adjust the sampling strategy between different time steps, and guide the model to pay more attention to the denser regions in the point cloud, thus improving the effectiveness of the model in point cloud recovery.Our PreDifPoint framework achieves more competitive results on various real-world datasets. Specifically, PreDifPoint achieves an overall accuracy of 87.96%, which is 0.35% higher than PointDif, on the classification task on PB-T50-395RS, a variant of ScanObjectNN dataset.
| null | null | null | null |
[] |
[] |
[] |
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] |
432
|
FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait
|
[
"Taekyung Ki",
"Dongchan Min",
"Gyeongsu Chae"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ki_FLOAT_Generative_Motion_Latent_Flow_Matching_for_Audio-driven_Talking_Portrait_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ki_FLOAT_Generative_Motion_Latent_Flow_Matching_for_Audio-driven_Talking_Portrait_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ki_FLOAT_Generative_Motion_ICCV_2025_supplemental.zip
|
@InProceedings{Ki_2025_ICCV,
author = {Ki, Taekyung and Min, Dongchan and Chae, Gyeongsu},
title = {FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {14699-14710}
}
|
With the rapid advancement of diffusion-based generative models, portrait image animation has achieved remarkable results. However, it still faces challenges in temporally consistent video generation and fast sampling due to its iterative sampling nature. This paper presents FLOAT, an audio-driven talking portrait video generation method based on flow matching generative model. Instead of a pixel-based latent space, we take advantage of a learned orthogonal motion latent space, enabling efficient generation and editing of temporally consistent motion. To achieve this, we introduce a transformer-based vector field predictor with an effective frame-wise conditioning mechanism. Additionally, our method supports speech-driven emotion enhancement, enabling a natural incorporation of expressive motions. Extensive experiments demonstrate that our method outperforms state-of-the-art audio-driven talking portrait methods in terms of visual quality, motion fidelity, and efficiency.
|
2412.01064
|
ICCV 2025. Project page:
https://deepbrainai-research.github.io/float/
| null | null |
[] |
[] |
[] |
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] |
433
|
Membership Inference Attacks with False Discovery Rate Control
|
[
"Chenxu Zhao",
"Wei Qian",
"Aobo Chen",
"Mengdi Huai"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhao_Membership_Inference_Attacks_with_False_Discovery_Rate_Control_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhao_Membership_Inference_Attacks_with_False_Discovery_Rate_Control_ICCV_2025_paper.pdf
| null |
@InProceedings{Zhao_2025_ICCV,
author = {Zhao, Chenxu and Qian, Wei and Chen, Aobo and Huai, Mengdi},
title = {Membership Inference Attacks with False Discovery Rate Control},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1216-1227}
}
|
Recent studies have shown that deep learning models are vulnerable to membership inference attacks (MIAs), which aim to infer whether a data record was used to train a target model or not. To analyze and study these vulnerabilities, various MIA methods have been proposed. Despite the significance and popularity of MIAs, existing works on MIAs are limited in providing guarantees on the false discovery rate (FDR), which refers to the expected proportion of false discoveries among the identified positive discoveries. However, it is very challenging to ensure the false discovery rate guarantees, because the underlying distribution is usually unknown, and the estimated non-member probabilities often exhibit interdependence. To tackle the above challenges, in this paper, we design a novel membership inference attack method, which can provide the guarantees on the false discovery rate. Additionally, we show that our method can also provide the marginal probability guarantee on labeling true non-member data as member data. Notably, our method can work as a wrapper that can be seamlessly integrated with existing MIA methods in a post-hoc manner, while also providing the FDR control. We perform the theoretical analysis for our method. Extensive experiments in various settings (e.g., the black-box setting and the lifelong learning setting) are also conducted to verify the desirable performance of our method.
|
2508.07066
| null | null | null |
[] |
[] |
[] |
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] |
434
|
EmbodiedSplat: Personalized Real-to-Sim-to-Real Navigation with Gaussian Splats from a Mobile Device
|
[
"Gunjan Chhablani",
"Xiaomeng Ye",
"Muhammad Zubair Irshad",
"Zsolt Kira"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chhablani_EmbodiedSplat_Personalized_Real-to-Sim-to-Real_Navigation_with_Gaussian_Splats_from_a_Mobile_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chhablani_EmbodiedSplat_Personalized_Real-to-Sim-to-Real_Navigation_with_Gaussian_Splats_from_a_Mobile_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chhablani_EmbodiedSplat_Personalized_Real-to-Sim-to-Real_ICCV_2025_supplemental.pdf
|
@InProceedings{Chhablani_2025_ICCV,
author = {Chhablani, Gunjan and Ye, Xiaomeng and Irshad, Muhammad Zubair and Kira, Zsolt},
title = {EmbodiedSplat: Personalized Real-to-Sim-to-Real Navigation with Gaussian Splats from a Mobile Device},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {25431-25441}
}
|
The field of Embodied AI predominantly relies on simulation for training and evaluation, often using either fully synthetic environments that lack photorealism or high-fidelity real-world reconstructions captured with expensive hardware. As a result, sim-to-real transfer remains a major challenge. In this paper, we introduce EmbodiedSplat, a novel approach that personalizes policy training by efficiently capturing the deployment environment and fine-tuning policies within the reconstructed scenes. Our method leverages 3D Gaussian Splatting (GS) and the Habitat-Sim simulator to bridge the gap between realistic scene capture and effective training environments. Using iPhone-captured deployment scenes, we reconstruct meshes via GS, enabling training in settings that closely approximate real-world conditions. We conduct a comprehensive analysis of training strategies, pre-training datasets, and mesh reconstruction techniques, evaluating their impact on sim-to-real predictivity in real-world scenarios. Experimental results demonstrate that agents fine-tuned with EmbodiedSplat outperform both zero-shot baselines pre-trained on large-scale real-world datasets (HM3D) and synthetically generated datasets (HSSD), achieving absolute success rate improvements of 20% and 40% on real-world Image Navigation task. Moreover, our approach yields a high sim-vs-real correlation (0.87-0.97) for the reconstructed meshes, underscoring its effectiveness in adapting policies to diverse environments with minimal effort. Project page: https://gchhablani.github.io/embodied-splat.
|
2509.17430
| null | null | null |
[] |
[] |
[] |
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] |
435
|
Sequential keypoint density estimator: an overlooked baseline of skeleton-based video anomaly detection
|
[
"Anja Delić",
"Matej Grcic",
"Siniša Šegvić"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Delic_Sequential_keypoint_density_estimator_an_overlooked_baseline_of_skeleton-based_video_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Delic_Sequential_keypoint_density_estimator_an_overlooked_baseline_of_skeleton-based_video_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Delic_Sequential_keypoint_density_ICCV_2025_supplemental.pdf
|
@InProceedings{Delic_2025_ICCV,
author = {Deli\'c, Anja and Grcic, Matej and \v{S}egvi\'c, Sini\v{s}a},
title = {Sequential keypoint density estimator: an overlooked baseline of skeleton-based video anomaly detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11579-11589}
}
|
Detecting anomalous human behaviouris an important visual taskin safety-critical applicationssuch as healthcare monitoring,workplace safety,or public surveillance.In these contexts,abnormalities are often reflectedwith unusual human poses.Thus, we propose SeeKer,a method for detecting anomaliesin sequences of human skeletons.Our method formulates the skeleton sequence densitythrough autoregressive factorization at the keypoint level.The corresponding conditional distributionsrepresent probable keypoint locations given prior skeletal motion.We formulate the joint distribution of the considered skeletonas causal prediction of conditional Gaussiansacross its constituent keypoints.A skeleton is flagged as anomalous if its keypoint locations surprise our model(i.e. receive a low density).In practice, our anomaly score is a weighted sum of per-keypoint log-conditionals,where the weights account for the confidence of the underlying keypoint detector.Despite its conceptual simplicity,SeeKer surpasses all previous methodson the UBnormal and MSAD-HR datasetswhile delivering competitive performanceon the ShanghaiTech dataset.
|
2506.18368
|
ICCV 2025 Highlight
| null | null |
[] |
[] |
[] |
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] |
436
|
SynCity: Training-Free Generation of 3D Worlds
|
[
"Paul Engstler",
"Aleksandar Shtedritski",
"Iro Laina",
"Christian Rupprecht",
"Andrea Vedaldi"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Engstler_SynCity_Training-Free_Generation_of_3D_Worlds_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Engstler_SynCity_Training-Free_Generation_of_3D_Worlds_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Engstler_SynCity_Training-Free_Generation_ICCV_2025_supplemental.zip
|
@InProceedings{Engstler_2025_ICCV,
author = {Engstler, Paul and Shtedritski, Aleksandar and Laina, Iro and Rupprecht, Christian and Vedaldi, Andrea},
title = {SynCity: Training-Free Generation of 3D Worlds},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27585-27595}
}
|
We propose SynCity, a method for generating explorable 3D worlds from textual descriptions. Our approach leverages pre-trained textual, image, and 3D generators without requiring fine-tuning or inference-time optimization. While most 3D generators are object-centric and unable to create large-scale worlds, we demonstrate how 2D and 3D generators can be combined to produce ever-expanding scenes. The world is generated tile by tile, with each new tile created within its context and seamlessly integrated into the scene. SynCity enables fine-grained control over the appearance and layout of the generated worlds, which are both detailed and diverse.
|
2503.16420
| null | null | null |
[] |
[] |
[] |
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437
|
Resonance: Learning to Predict Social-Aware Pedestrian Trajectories as Co-Vibrations
|
[
"Conghao Wong",
"Ziqian Zou",
"Beihao Xia"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wong_Resonance_Learning_to_Predict_Social-Aware_Pedestrian_Trajectories_as_Co-Vibrations_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wong_Resonance_Learning_to_Predict_Social-Aware_Pedestrian_Trajectories_as_Co-Vibrations_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wong_Resonance_Learning_to_ICCV_2025_supplemental.pdf
|
@InProceedings{Wong_2025_ICCV,
author = {Wong, Conghao and Zou, Ziqian and Xia, Beihao},
title = {Resonance: Learning to Predict Social-Aware Pedestrian Trajectories as Co-Vibrations},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {25788-25799}
}
|
Learning to forecast trajectories of intelligent agents has caught much more attention recently. However, it remains a challenge to accurately account for agents' intentions and social behaviors when forecasting, and in particular, to simulate the unique randomness within each of those components in an explainable and decoupled way. Inspired by vibration systems and their resonance properties, we propose the Resonance (short for Re) model to encode and forecast pedestrian trajectories in the form of "co-vibrations". It decomposes trajectory modifications and randomness into multiple vibration portions to simulate agents' reactions to each single cause, and forecasts trajectories as the superposition of these independent vibrations separately. Also, benefiting from such vibrations and their spectral properties, representations of social interactions can be learned by emulating the resonance phenomena, further enhancing its explainability. Experiments on multiple datasets have verified its usefulness both quantitatively and qualitatively.
|
2412.02447
| null | null |
https://cocoon2wong.github.io/Re/
|
[] |
[] |
[] |
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] |
438
|
Adversarial Training for Probabilistic Robustness
|
[
"Yi Zhang",
"Yuhang Chen",
"Zhen Chen",
"Wenjie Ruan",
"Xiaowei Huang",
"Siddartha Khastgir",
"Xingyu Zhao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_Adversarial_Training_for_Probabilistic_Robustness_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_Adversarial_Training_for_Probabilistic_Robustness_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_Adversarial_Training_for_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Yi and Chen, Yuhang and Chen, Zhen and Ruan, Wenjie and Huang, Xiaowei and Khastgir, Siddartha and Zhao, Xingyu},
title = {Adversarial Training for Probabilistic Robustness},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1675-1685}
}
|
Deep learning (DL) has shown transformative potential across industries, yet its sensitivity to adversarial examples (AEs) limits its reliability and broader deployment. Research on DL robustness has developed various techniques, with adversarial training (AT) established as a leading approach to counter AEs. Traditional AT focuses on worst-case robustness (WCR), but recent work has introduced probabilistic robustness (PR), which evaluates the proportion of AEs within a local perturbation range, providing an overall assessment of the model's local robustness and acknowledging residual risks that are more practical to manage. However, existing AT methods are fundamentally designed to improve WCR, and no dedicated methods currently target PR. To bridge this gap, we reformulate a new min-max optimization as the theoretical foundation for AT focused on PR, and introduce an AT-PR training scheme with effective numerical algorithms to solve the new optimization problem. Our experiments, based on 38 DL models trained on common datasets and architectures, demonstrate that AT-PR achieves higher improvements in PR than AT-WCR methods and shows more consistent effectiveness across varying local inputs, with a smaller trade-off in model generalization. Open-source tools and all experiments are publicly accessible.
| null | null | null | null |
[] |
[] |
[] |
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] |
439
|
MUNBa: Machine Unlearning via Nash Bargaining
|
[
"Jing Wu",
"Mehrtash Harandi"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wu_MUNBa_Machine_Unlearning_via_Nash_Bargaining_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wu_MUNBa_Machine_Unlearning_via_Nash_Bargaining_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wu_MUNBa_Machine_Unlearning_ICCV_2025_supplemental.pdf
|
@InProceedings{Wu_2025_ICCV,
author = {Wu, Jing and Harandi, Mehrtash},
title = {MUNBa: Machine Unlearning via Nash Bargaining},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4754-4765}
}
|
Machine Unlearning (MU) aims to selectively erase harmful behaviors from models while retaining the overall utility of the model. As a multi-task learning problem, MU involves balancing objectives related to forgetting specific concepts/data and preserving general performance. A naive integration of these forgetting and preserving objectives can lead to gradient conflicts and dominance, impeding MU algorithms from reaching optimal solutions.To address the gradient conflict and dominance issue, we reformulate MU as a two-player cooperative game, where the two players, namely, the forgetting player and the preservation player, contribute via their gradient proposals to maximize their overall gain and balance their contributions.To this end, inspired by the Nash bargaining theory, we derive a closed-form solution to guide the model toward the Pareto stationary point.Our formulation of MU guarantees an equilibrium solution, where any deviation from the final state would lead to a reduction in the overall objectives for both players, ensuring optimality in each objective.We evaluate our algorithm's effectiveness on a diverse set of tasks across image classification and image generation.Extensive experiments with ResNet, vision-language model CLIP, and text-to-image diffusion models demonstrate that our method outperforms state-of-the-art MU algorithms, achieving a better trade-off between forgetting and preserving.Our results also highlight improvements in forgetting precision, preservation of generalization, and robustness against adversarial attacks.
|
2411.15537
| null | null | null |
[] |
[] |
[] |
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] |
440
|
DLF: Extreme Image Compression with Dual-generative Latent Fusion
|
[
"Naifu Xue",
"Zhaoyang Jia",
"Jiahao Li",
"Bin Li",
"Yuan Zhang",
"Yan Lu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xue_DLF_Extreme_Image_Compression_with_Dual-generative_Latent_Fusion_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xue_DLF_Extreme_Image_Compression_with_Dual-generative_Latent_Fusion_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xue_DLF_Extreme_Image_ICCV_2025_supplemental.pdf
|
@InProceedings{Xue_2025_ICCV,
author = {Xue, Naifu and Jia, Zhaoyang and Li, Jiahao and Li, Bin and Zhang, Yuan and Lu, Yan},
title = {DLF: Extreme Image Compression with Dual-generative Latent Fusion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {19227-19236}
}
|
Recent studies in extreme image compression have achieved remarkable performance by compressing the tokens from generative tokenizers. However, these methods often prioritize clustering common semantics within the dataset, while overlooking the diverse details of individual objects. Consequently, this results in suboptimal reconstruction fidelity, especially at low bitrates. To address this issue, we introduce a Dual-generative Latent Fusion (DLF) paradigm. DLF decomposes the latent into semantic and detail elements, compressing them through two distinct branches. The semantic branch clusters high-level information into compact tokens, while the detail branch encodes perceptually critical details to enhance the overall fidelity. Additionally, we propose a cross-branch interactive design to reduce redundancy between the two branches, thereby minimizing the overall bit cost. Experimental results demonstrate the impressive reconstruction quality of DLF even below 0.01 bits per pixel (bpp). On the CLIC2020 test set, our method achieves bitrate savings of up to 27.93% on LPIPS and 53.55% on DISTS compared to MS-ILLM. Furthermore, DLF surpasses recent diffusion-based codecs in visual fidelity while maintaining a comparable level of generative realism. Project: https://dlfcodec.github.io/
|
2503.01428
| null | null |
https://dlfcodec.github.io/
|
[] |
[] |
[] |
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] |
441
|
PanoLlama: Generating Endless and Coherent Panoramas with Next-Token-Prediction LLMs
|
[
"Teng Zhou",
"Xiaoyu Zhang",
"Yongchuan Tang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhou_PanoLlama_Generating_Endless_and_Coherent_Panoramas_with_Next-Token-Prediction_LLMs_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_PanoLlama_Generating_Endless_and_Coherent_Panoramas_with_Next-Token-Prediction_LLMs_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhou_PanoLlama_Generating_Endless_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhou_2025_ICCV,
author = {Zhou, Teng and Zhang, Xiaoyu and Tang, Yongchuan},
title = {PanoLlama: Generating Endless and Coherent Panoramas with Next-Token-Prediction LLMs},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {15340-15349}
}
|
Panoramic Image Generation (PIG) aims to create coherent images of arbitrary lengths. Most existing methods fall in the joint diffusion paradigm, but their complex and heuristic crop connection designs often limit their ability to achieve multilevel coherence. By deconstructing this challenge into its core components, we find it naturally aligns with next-token prediction, leading us to adopt an autoregressive (AR) paradigm for PIG modeling. However, existing visual AR (VAR) models are limited to fixed-size generation, lacking the capability to produce panoramic images. In this paper, we propose PanoLlama, a novel framework that achieves endless and coherent panorama generation with the autoregressive paradigm. Our approach develops a training-free strategy that utilizes token redirection to overcome the size limitations of existing VAR models, enabling next-crop prediction in both horizontal and vertical directions. This refreshes the PIG pipeline while achieving SOTA performance in coherence (47.50%), fidelity(28.16%), and aesthetics (15%). Additionally, PanoLlama supports applications other PIG methods cannot achieve, including mask-free layout control, multi-scale and multi-guidance synthesis. To facilitate standardized evaluation, we also establish a dataset with 1,000 prompts spanning 100+ themes, providing a new testing benchmark for PIG research.
|
2411.15867
| null |
https://github.com/0606zt/PanoLlama
| null |
[] |
[] |
[] |
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] |
442
|
Leaps and Bounds: An Improved Point Cloud Winding Number Formulation for Fast Normal Estimation and Surface Reconstruction
|
[
"Chamin Hewa Koneputugodage",
"Dylan Campbell",
"Stephen Gould"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Koneputugodage_Leaps_and_Bounds_An_Improved_Point_Cloud_Winding_Number_Formulation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Koneputugodage_Leaps_and_Bounds_An_Improved_Point_Cloud_Winding_Number_Formulation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Koneputugodage_Leaps_and_Bounds_ICCV_2025_supplemental.pdf
|
@InProceedings{Koneputugodage_2025_ICCV,
author = {Koneputugodage, Chamin Hewa and Campbell, Dylan and Gould, Stephen},
title = {Leaps and Bounds: An Improved Point Cloud Winding Number Formulation for Fast Normal Estimation and Surface Reconstruction},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26116-26125}
}
|
Recent methods for point cloud surface normal estimation predominantly use the generalized winding number field induced by the normals. Optimizing the field towards satisfying desired properties, such as the input points being on the surface defined by the field, provides a principled way to obtain globally consistent surface normals. However, we show that the existing winding number formulation for point clouds is a poor approximation near the input surface points, diverging as the query point approaches a surface point. This is problematic for methods that rely on the accuracy and stability of this approximation, requiring heuristics to compensate. Instead, we derive a more accurate approximation that is properly bounded and converges to the correct value. We then examine two distinct approaches that optimize for globally consistent normals using point cloud winding numbers. We show how the original unbounded formulation influences key design choices in both methods and demonstrate that substituting our formulation yields substantive improvements with respect to normal estimation and surface reconstruction accuracy.
| null | null | null | null |
[] |
[] |
[] |
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] |
443
|
VQ-SGen: A Vector Quantized Stroke Representation for Creative Sketch Generation
|
[
"Jiawei Wang",
"Zhiming Cui",
"Changjian Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_VQ-SGen_A_Vector_Quantized_Stroke_Representation_for_Creative_Sketch_Generation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_VQ-SGen_A_Vector_Quantized_Stroke_Representation_for_Creative_Sketch_Generation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_VQ-SGen_A_Vector_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Jiawei and Cui, Zhiming and Li, Changjian},
title = {VQ-SGen: A Vector Quantized Stroke Representation for Creative Sketch Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {19311-19320}
}
|
This paper presents VQ-SGen, a novel algorithm for high-quality creative sketch generation. Recent approaches have framed the task as pixel-based generation either as a whole or part-by-part, neglecting the intrinsic and contextual relationships among individual strokes, such as the shape and spatial positioning of both proximal and distant strokes. To overcome these limitations, we propose treating each stroke within a sketch as an entity and introducing a vector-quantized (VQ) stroke representation for fine-grained sketch generation. Our method follows a two-stage framework - in stage one, we decouple each stroke's shape and location information to ensure the VQ representation prioritizes stroke shape learning. In stage two, we feed the precise and compact representation into an auto-decoding Transformer to incorporate stroke semantics, positions, and shapes into the generation process. By utilizing tokenized stroke representation, our approach generates strokes with high fidelity and facilitates novel applications, such as text or class label conditioned generation and sketch completion. Comprehensive experiments demonstrate our method surpasses existing state-of-the-art techniques on the CreativeSketch dataset, underscoring its effectiveness.
| null | null | null |
https://enigma-li.github.io/projects/VQ-SGen/VQ-SGen.html
|
[] |
[] |
[] |
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] |
444
|
Cross-Category Subjectivity Generalization for Style-Adaptive Sketch Re-ID
|
[
"Zechao Hu",
"Zhengwei Yang",
"Hao Li",
"Zheng Wang",
"Yixiong Zou"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Hu_Cross-Category_Subjectivity_Generalization_for_Style-Adaptive_Sketch_Re-ID_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Hu_Cross-Category_Subjectivity_Generalization_for_Style-Adaptive_Sketch_Re-ID_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Hu_Cross-Category_Subjectivity_Generalization_ICCV_2025_supplemental.pdf
|
@InProceedings{Hu_2025_ICCV,
author = {Hu, Zechao and Yang, Zhengwei and Li, Hao and Wang, Zheng and Zou, Yixiong},
title = {Cross-Category Subjectivity Generalization for Style-Adaptive Sketch Re-ID},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {22644-22653}
}
|
Sketch-based person re-identification (re-ID) enables pedestrian retrieval using sketches. While recent methods have improved modality alignment between sketches and RGB images, the challenge of subjective style variation, where sketches exhibit diverse and unpredictable appearances, remains largely unresolved.A natural solution is to train on a diverse range of pedestrian sketches, but the high cost of large-scale pedestrian sketch collection makes this impractical.In contrast, sketches of general categories (e.g., animals, objects) exhibit diverse style variations and are accessible at a low cost, making them an intuitive and scalable alternative for enhancing style generalization in sketch re-ID.To this end, we propose Adaptive Incremental Prompt-tuning (AIP), the first approach that explores cross-category subjective style generalization for sketch re-ID. Specifically, AIP incorporates a multi-stage prompt-tuning strategy that learns a broad but shareable spectrum of sketch styles from non-pedestrian data. In addition, an input-sensitive prompt generator enables the model to adapt dynamically to unseen sketch styles.Extensive experimental results demonstrate that the performance gain is not merely attributed to the inclusion of additional data but rather to the effectiveness of AIP in leveraging non-pedestrian data for subjective style generalization. Our method outperforms existing works by a significant margin, establishing new state-of-the-art results.
| null | null | null | null |
[] |
[] |
[] |
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] |
445
|
VolumetricSMPL: A Neural Volumetric Body Model for Efficient Interactions, Contacts, and Collisions
|
[
"Marko Mihajlovic",
"Siwei Zhang",
"Gen Li",
"Kaifeng Zhao",
"Lea Muller",
"Siyu Tang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Mihajlovic_VolumetricSMPL_A_Neural_Volumetric_Body_Model_for_Efficient_Interactions_Contacts_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Mihajlovic_VolumetricSMPL_A_Neural_Volumetric_Body_Model_for_Efficient_Interactions_Contacts_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Mihajlovic_VolumetricSMPL_A_Neural_ICCV_2025_supplemental.pdf
|
@InProceedings{Mihajlovic_2025_ICCV,
author = {Mihajlovic, Marko and Zhang, Siwei and Li, Gen and Zhao, Kaifeng and Muller, Lea and Tang, Siyu},
title = {VolumetricSMPL: A Neural Volumetric Body Model for Efficient Interactions, Contacts, and Collisions},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {5060-5070}
}
|
Parametric human body models play a crucial role in computer graphics and vision, enabling applications ranging from human motion analysis to understanding human-environment interactions. Traditionally, these models use surface meshes, which pose challenges in efficiently handling interactions with other geometric entities, such as objects and scenes, typically represented as meshes or point clouds. To address this limitation, recent research has explored volumetric neural implicit body models. However, existing works are either insufficiently robust for complex human articulations or impose high computational and memory costs, limiting their widespread use. To this end, we introduce VolumetricSMPL, a neural volumetric body model that leverages Neural Blend Weights (NBW) to generate compact, yet efficient MLP decoders. Unlike prior approaches that rely on large MLPs, NBW dynamically blends a small set of learned weight matrices using predicted shape- and pose-dependent coefficients, significantly improving computational efficiency while preserving expressiveness. VolumetricSMPL outperforms prior volumetric occupancy model COAP with 10x faster inference, 6x lower GPU memory usage, enhanced accuracy, and a Signed Distance Function (SDF) for efficient and differentiable contact modeling. We demonstrate VolumetricSMPL's strengths across four challenging tasks: (1) reconstructing human-object interactions from in-the-wild images, (2) recovering human meshes in 3D scenes from egocentric views, (3) scene-constrained motion synthesis, and (4) resolving self-intersections. Our results highlight its broad applicability and significant performance and efficiency gains.
|
2506.23236
|
[ICCV 2025] https://markomih.github.io/VolumetricSMPL
| null | null |
[] |
[] |
[] |
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] |
446
|
Spatial-Temporal Forgery Trace based Forgery Image Identification
|
[
"Yilin Wang",
"Zunlei Feng",
"Jiachi Wang",
"Hengrui Lou",
"Binjia Zhou",
"Jie Lei",
"Mingli Song",
"Yijun Bei"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Spatial-Temporal_Forgery_Trace_based_Forgery_Image_Identification_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Spatial-Temporal_Forgery_Trace_based_Forgery_Image_Identification_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_Spatial-Temporal_Forgery_Trace_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Yilin and Feng, Zunlei and Wang, Jiachi and Lou, Hengrui and Zhou, Binjia and Lei, Jie and Song, Mingli and Bei, Yijun},
title = {Spatial-Temporal Forgery Trace based Forgery Image Identification},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {17067-17076}
}
|
The rapid development of AIGC technology has enabled highly realistic forged images to deceive human perception, posing serious risks across many areas. Current deepfake image detection methods primarily identify forgeries by extracting handcrafted features, deep features, and frequency-domain features. While these features contain forgery traces, they also include a substantial amount of the image's semantic information, which interferes with the precision and generalization of forgery detection models. To tackle these challenges, this paper introduces a novel forgery image identification method based on the Spatial-Temporal Forgery Trace (STFT). Motivated by the fact that forgery images are more easily fitted to a specific distribution than real images, the STFT method approaches the issue from a forged image distribution modeling perspective, employing generative diffusion models to meticulously capture the temporal distribution of images. It further models the relationship between temporal feature variations and spatially corresponding temporal features, treating them as temporal and spatial forgery traces. Moreover, STFT incorporates frequency-domain features as weighting factors to accelerate the localization of spatio-temporal forgery traces. Experiments demonstrate that by integrating spatial, temporal, and frequency perspectives within the latent space, STFT effectively captures subtle spatio-temporal forgery traces, exhibiting strong robustness and generalizability. It outperforms state-of-the-art methods on major benchmark datasets in the field. The source code for STFT is available at \href https://anonymous.4open.science/r/STFT-B552/ .
| null | null | null | null |
[] |
[] |
[] |
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] |
447
|
Passing the Driving Knowledge Test
|
[
"Maolin Wei",
"Wanzhou Liu",
"Eshed Ohn-Bar"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wei_Passing_the_Driving_Knowledge_Test_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wei_Passing_the_Driving_Knowledge_Test_ICCV_2025_paper.pdf
| null |
@InProceedings{Wei_2025_ICCV,
author = {Wei, Maolin and Liu, Wanzhou and Ohn-Bar, Eshed},
title = {Passing the Driving Knowledge Test},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {8395-8406}
}
|
If a Large Language Model (LLM) were to take a driving knowledge test today, would it pass? Beyond standard spatial and visual question-answering (QA) tasks on current autonomous driving benchmarks, driving knowledge tests require a complete understanding of all traffic rules, signage, and right-of-way principles. To pass this test, human drivers must discern various edge cases that rarely appear in real-world datasets. In this work, we present DriveQA, an extensive open-source text and vision-based benchmark that exhaustively covers traffic regulations and scenarios. Through our experiments using DriveQA, we show that (1) state-of-the-art LLMs and Multimodal LLMs (MLLMs) perform well on basic traffic rules but exhibit significant weaknesses in numerical reasoning and complex right-of-way scenarios, traffic sign variations, and spatial layouts, (2) fine-tuning on DriveQA improves accuracy across multiple categories, particularly in regulatory sign recognition and intersection decision-making, (3) controlled variations in DriveQA-V provide insights into model sensitivity to environmental factors such as lighting, perspective, distance, and weather conditions, and (4) pretraining on DriveQA enhances downstream driving task performance, leading to improved results on real-world datasets such as nuScenes and BDD, while also demonstrating that models can internalize text and synthetic traffic knowledge to generalize effectively across downstream QA tasks.
|
2508.21824
|
Accepted by ICCV 2025. Project page: https://driveqaiccv.github.io/
| null | null |
[] |
[] |
[
"DriveQA/DriveQA_Dataset"
] |
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448
|
OneGT: One-Shot Geometry-Texture Neural Rendering for Head Avatars
|
[
"Jinshu Chen",
"Bingchuan Li",
"Fan Zhang",
"Songtao Zhao",
"Qian He"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_OneGT_One-Shot_Geometry-Texture_Neural_Rendering_for_Head_Avatars_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_OneGT_One-Shot_Geometry-Texture_Neural_Rendering_for_Head_Avatars_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_OneGT_One-Shot_Geometry-Texture_ICCV_2025_supplemental.pdf
|
@InProceedings{Chen_2025_ICCV,
author = {Chen, Jinshu and Li, Bingchuan and Zhang, Fan and Zhao, Songtao and He, Qian},
title = {OneGT: One-Shot Geometry-Texture Neural Rendering for Head Avatars},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11294-11304}
}
|
Existing solutions for creating high-fidelity digital head avatars encounter various obstacles. Traditional rendering tools offer realistic results, while heavily requiring expert skills. Neural rendering methods are more efficient but often compromise between the generated fidelity and flexibility. We present OneGT that adheres to the frameworks of the rendering tools, while restructuring individual stages of the rendering pipeline through neural networks. OneGT maintains high systemic interpretability, inheriting the superior performances of neural rendering approaches. Specifically, OneGT contains a skeleton-anchoring stage and a texture-rendering stage, in which well-designed Transformers learn the geometric transformations and the proposed reference-perceptible DiT renders the textures respectively. Our framework learns geometric consistency from the innovatively introduced synthetic data, thus achieving superior performance while requiring only 10%-30% of the real-world data typically used by competitive methods. Experimental results demonstrate that OneGT achieves high fidelity in producing portrait avatars, meanwhile maintaining the flexibility of editing.
| null | null | null | null |
[] |
[] |
[] |
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] |
449
|
Joint Asymmetric Loss for Learning with Noisy Labels
|
[
"Jialiang Wang",
"Xianming Liu",
"Xiong Zhou",
"Gangfeng Hu",
"Deming Zhai",
"Junjun Jiang",
"Xiangyang Ji"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Joint_Asymmetric_Loss_for_Learning_with_Noisy_Labels_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Joint_Asymmetric_Loss_for_Learning_with_Noisy_Labels_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_Joint_Asymmetric_Loss_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Jialiang and Liu, Xianming and Zhou, Xiong and Hu, Gangfeng and Zhai, Deming and Jiang, Junjun and Ji, Xiangyang},
title = {Joint Asymmetric Loss for Learning with Noisy Labels},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1947-1956}
}
|
Learning with noisy labels is a crucial task for training accurate deep neural networks. To mitigate label noise, prior studies have proposed various robust loss functions, particularly symmetric losses. Nevertheless, symmetric losses usually suffer from the underfitting issue due to the overly strict constraint. To address this problem, the Active Passive Loss (APL) jointly optimizes an active and a passive loss to mutually enhance the overall fitting ability. Within APL, symmetric losses have been successfully extended, yielding advanced robust loss functions. Despite these advancements, emerging theoretical analyses indicate that asymmetric losses, a new class of robust loss functions, possess superior properties compared to symmetric losses. However, existing asymmetric losses are not compatible with advanced optimization frameworks such as APL, limiting their potential and applicability. Motivated by this theoretical gap and the prospect of asymmetric losses, we extend the asymmetric loss to the more complex passive loss scenario and propose the Asymetric Mean Square Error (AMSE), a novel asymmetric loss. We rigorously establish the necessary and sufficient condition under which AMSE satisfies the asymmetric condition. By substituting the traditional symmetric passive loss in APL with our proposed AMSE, we introduce a novel robust loss framework termed Joint Asymmetric Loss (JAL). Extensive experiments demonstrate the effectiveness of our method in mitigating label noise. Code available at: https://github.com/cswjl/joint-asymmetric-loss
|
2507.17692
|
Accepted by ICCV 2025
|
https://github.com/cswjl/joint-asymmetric-loss
| null |
[] |
[] |
[] |
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450
|
A Unified Framework for Industrial Cel-Animation Colorization with Temporal-Structural Awareness
|
[
"Xiaoyi Feng",
"Tao Huang",
"Peng Wang",
"Zizhou Huang",
"Zhang Haihang",
"Yuntao Zou",
"Dagang Li",
"Kaifeng Zou"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Feng_A_Unified_Framework_for_Industrial_Cel-Animation_Colorization_with_Temporal-Structural_Awareness_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Feng_A_Unified_Framework_for_Industrial_Cel-Animation_Colorization_with_Temporal-Structural_Awareness_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Feng_A_Unified_Framework_ICCV_2025_supplemental.pdf
|
@InProceedings{Feng_2025_ICCV,
author = {Feng, Xiaoyi and Huang, Tao and Wang, Peng and Huang, Zizhou and Haihang, Zhang and Zou, Yuntao and Li, Dagang and Zou, Kaifeng},
title = {A Unified Framework for Industrial Cel-Animation Colorization with Temporal-Structural Awareness},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {19301-19310}
}
|
Line drawing colorization is a critical step in the cel-animation industry, where artists use a paint bucket tool to apply RGB values to segments based on a character's color design sheet. Current automated methods predominantly focus on consecutive frame colorization, using a single adjacent frame as a reference. These approaches often face two major challenges: inaccurate segment colorization due to significant deformations between the target and reference frames, and incomplete information in a single frame that prevents finding suitable reference segments, leading to poor color accuracy. To address these challenges, we propose a novel colorization framework that integrates both temporal and structural information. Using multiple reference keyframes, our method effectively captures temporal information across frames, enhancing the accuracy of colorization for transitional frames. In addition, we leverage structural information through a matching-based approach that ensures precise segment alignment across frames. This combination of temporal awareness through multi-frame references and structural alignment improves colorization robustness, even in scenarios with large motion and deformations. Our method outperforms existing techniques, demonstrating superior colorization accuracy and consistency in industrial cel-animation workflows.
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[] |
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] |
451
|
Uncalibrated Structure from Motion on a Sphere
|
[
"Jonathan Ventura",
"Viktor Larsson",
"Fredrik Kahl"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ventura_Uncalibrated_Structure_from_Motion_on_a_Sphere_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ventura_Uncalibrated_Structure_from_Motion_on_a_Sphere_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ventura_Uncalibrated_Structure_from_ICCV_2025_supplemental.zip
|
@InProceedings{Ventura_2025_ICCV,
author = {Ventura, Jonathan and Larsson, Viktor and Kahl, Fredrik},
title = {Uncalibrated Structure from Motion on a Sphere},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {69-78}
}
|
Spherical motion is a special case of camera motion where the camera moves on the imaginary surface of a sphere with the optical axis normal to the surface. Common sources of spherical motion are a person capturing a stereo panorama with a phone held in an outstretched hand, or a hemi-spherical camera rig used for multi-view scene capture. However, traditional structure-from-motion pipelines tend to fail on spherical camera motion sequences, especially when the camera is facing outward. Building upon prior work addressing the calibrated case, we explore uncalibrated reconstruction from spherical motion, assuming a fixed but unknown focal length parameter. We show that, although two-view spherical motion is always a critical case, self-calibration is possible from three or more views. Through analysis of the relationship between focal length and spherical relative pose, we devise a global structure-from-motion approach for uncalibrated reconstruction. We demonstrate the effectiveness of our approach on real-world captures in various settings, even when the camera motion deviates from perfect spherical motion. Code and data for our method are available at https://github.com/jonathanventura/spherical-sfm.
| null | null | null | null |
[] |
[] |
[] |
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] |
452
|
CARL: Causality-guided Architecture Representation Learning for an Interpretable Performance Predictor
|
[
"Han Ji",
"Yuqi Feng",
"Jiahao Fan",
"Yanan Sun"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ji_CARL_Causality-guided_Architecture_Representation_Learning_for_an_Interpretable_Performance_Predictor_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ji_CARL_Causality-guided_Architecture_Representation_Learning_for_an_Interpretable_Performance_Predictor_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ji_CARL_Causality-guided_Architecture_ICCV_2025_supplemental.pdf
|
@InProceedings{Ji_2025_ICCV,
author = {Ji, Han and Feng, Yuqi and Fan, Jiahao and Sun, Yanan},
title = {CARL: Causality-guided Architecture Representation Learning for an Interpretable Performance Predictor},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {23019-23029}
}
|
Performance predictors have emerged as a promising method to accelerate the evaluation stage of neural architecture search (NAS). These predictors estimate the performance of unseen architectures by learning from the correlation between a small set of trained architectures and their performance. However, most existing predictors ignore the inherent distribution shift between limited training samples and diverse test samples. Hence, they tend to learn spurious correlations as shortcuts to predictions, leading to poor generalization. To address this, we propose a Causality-guided Architecture Representation Learning (CARL) method aiming to separate critical (causal) and redundant (non-causal) features of architectures for generalizable architecture performance prediction. Specifically, we employ a substructure extractor to split the input architecture into critical and redundant substructures in the latent space. Then, we generate multiple interventional samples by pairing critical representations with diverse redundant representations to prioritize critical features. Extensive experiments on five NAS search spaces demonstrate the state-of-the-art accuracy and superior interpretability of CARL. For instance, CARL achieves 97.67% top-1 accuracy on CIFAR-10 using DARTS.
|
2506.04001
| null | null | null |
[] |
[] |
[] |
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] |
453
|
An OpenMind for 3D Medical Vision Self-supervised Learning
|
[
"Tassilo Wald",
"Constantin Ulrich",
"Jonathan Suprijadi",
"Sebastian Ziegler",
"Michal Nohel",
"Robin Peretzke",
"Gregor Kohler",
"Klaus Maier-Hein"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wald_An_OpenMind_for_3D_Medical_Vision_Self-supervised_Learning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wald_An_OpenMind_for_3D_Medical_Vision_Self-supervised_Learning_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wald_An_OpenMind_for_ICCV_2025_supplemental.pdf
|
@InProceedings{Wald_2025_ICCV,
author = {Wald, Tassilo and Ulrich, Constantin and Suprijadi, Jonathan and Ziegler, Sebastian and Nohel, Michal and Peretzke, Robin and Kohler, Gregor and Maier-Hein, Klaus},
title = {An OpenMind for 3D Medical Vision Self-supervised Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {23839-23879}
}
|
The field of self-supervised learning (SSL) for 3D medical images lacks consistency and standardization. While many methods have been developed, it is impossible to identify the current state-of-the-art, due to i) varying and small pretraining datasets, ii) varying architectures, and iii) being evaluated on differing downstream datasets. In this paper, we bring clarity to this field and lay the foundation for further method advancements through three key contributions: We a) publish the largest publicly available pre-training dataset comprising 114k 3D brain MRI volumes, enabling all practitioners to pre-train on a large-scale dataset. We b) benchmark existing 3D self-supervised learning methods on this dataset for a state-of-the-art CNN and Transformer architecture, clarifying the state of 3D SSL pre-training. Among many findings, we show that pre-trained methods can exceed a strong from-scratch nnU-Net ResEnc-L baseline. Lastly, we c) publish the code of our pre-training and fine-tuning frameworks and provide the pre-trained models created during the benchmarking process to facilitate rapid adoption and reproduction.
|
2412.17041
|
Pre-Print; Dataset, Benchmark and Codebase available through
https://github.com/MIC-DKFZ/nnssl
| null | null |
[] |
[
"AnonRes/PrimusM-OpenMind-MAE"
] |
[
"liamchalcroft/OpenMind2D"
] |
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] |
454
|
Online Dense Point Tracking with Streaming Memory
|
[
"Qiaole Dong",
"Yanwei Fu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Dong_Online_Dense_Point_Tracking_with_Streaming_Memory_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Dong_Online_Dense_Point_Tracking_with_Streaming_Memory_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Dong_Online_Dense_Point_ICCV_2025_supplemental.pdf
|
@InProceedings{Dong_2025_ICCV,
author = {Dong, Qiaole and Fu, Yanwei},
title = {Online Dense Point Tracking with Streaming Memory},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {8710-8720}
}
|
Dense point tracking is a challenging task requiring the continuous tracking of every point in the initial frame throughout a substantial portion of a video, even in the presence of occlusions. Traditional methods use optical flow models to directly estimate long-range motion, but they often suffer from appearance drifting without considering temporal consistency. Recent point tracking algorithms usually depend on sliding windows for indirect information propagation from the first frame to the current one, which is slow and less effective for long-range tracking. To account for temporal consistency and enable efficient information propagation, we present a lightweight and fast model with Streaming memory for dense POint Tracking and online video processing. The SPOT framework features three core components: a customized memory reading module for feature enhancement, a sensory memory for short-term motion dynamics modeling, and a visibility-guided splatting module for accurate information propagation. This combination enables SPOT to perform dense point tracking with state-of-the-art accuracy on the CVO benchmark, as well as comparable or superior performance to offline models on sparse tracking benchmarks such as TAP-Vid and RoboTAP. Notably, SPOT with 10x smaller parameter numbers operates at least 2x faster than previous state-of-the-art models while maintaining the best performance on CVO. We will release the models and codes at: https://dqiaole.github.io/SPOT/.
|
2503.06471
|
ICCV 2025
| null |
https://dqiaole.github.io/SPOT/
|
[] |
[] |
[] |
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455
|
MCID: Multi-aspect Copyright Infringement Detection for Generated Images
|
[
"Chuanwei Huang",
"Zexi Jia",
"Hongyan Fei",
"Yeshuang Zhu",
"Zhiqiang Yuan",
"Ying Deng",
"Jiapei Zhang",
"Xiaoyue Duan",
"Jinchao Zhang",
"Jie Zhou"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Huang_MCID_Multi-aspect_Copyright_Infringement_Detection_for_Generated_Images_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Huang_MCID_Multi-aspect_Copyright_Infringement_Detection_for_Generated_Images_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Huang_MCID_Multi-aspect_Copyright_ICCV_2025_supplemental.pdf
|
@InProceedings{Huang_2025_ICCV,
author = {Huang, Chuanwei and Jia, Zexi and Fei, Hongyan and Zhu, Yeshuang and Yuan, Zhiqiang and Deng, Ying and Zhang, Jiapei and Duan, Xiaoyue and Zhang, Jinchao and Zhou, Jie},
title = {MCID: Multi-aspect Copyright Infringement Detection for Generated Images},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {16154-16164}
}
|
With the rapid advancement of generative models, we can now create highly realistic images. This represents a significant technical breakthrough but also introduces new challenges for copyright protection. Previous methods for detecting copyright infringement in AI-generated images mainly depend on global similarity. However, real-world infringement often occurs only on certain attributes rather than being a global infringement. To address these challenges, we propose a novel Multi-aspect Copyright Infringement Detection (MCID) task, which encompasses various types of infringement, including content, style, structure, and intellectual property infringement. We further develop the Hybrid Infringement Detection Model (HIDM) to address the MCID task. By combining feature-based methods with VLMs, it enables the detection of various infringement types and provides interpretable results. To ensure the MCID task meets actual legal requirements, we construct a Large-Scale Copyright Dataset (LSCD) with clear author copyright ownership. Based on LSCD, we provide a benchmark annotated by legal experts for performance evaluation. Experimental results show that HIDM effectively detects various types of image copyright infringement and offers a more interpretable and superior solution compared to previous methods.
| null | null | null | null |
[] |
[] |
[] |
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] |
456
|
Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks
|
[
"Hailong Guo",
"Bohan Zeng",
"Yiren Song",
"Wentao Zhang",
"Jiaming Liu",
"Chuang Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Guo_Any2AnyTryon_Leveraging_Adaptive_Position_Embeddings_for_Versatile_Virtual_Clothing_Tasks_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Guo_Any2AnyTryon_Leveraging_Adaptive_Position_Embeddings_for_Versatile_Virtual_Clothing_Tasks_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Guo_Any2AnyTryon_Leveraging_Adaptive_ICCV_2025_supplemental.pdf
|
@InProceedings{Guo_2025_ICCV,
author = {Guo, Hailong and Zeng, Bohan and Song, Yiren and Zhang, Wentao and Liu, Jiaming and Zhang, Chuang},
title = {Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {19085-19096}
}
|
Image-based virtual try-on (VTON) aims to generate a virtual try-on result by transferring an input garment onto a target person's image. However, the scarcity of paired garment-model data makes it challenging for existing meth ods to achieve high generalization and quality in VTON.Also, it limits the ability to generate mask-free try-ons. To tackle the data scarcity problem, approaches such as Stable Garment and MMTryon use a synthetic data strategy, effectively increasing the amount of paired data on the model side. However, existing methods are typically limited to performing specific try-on tasks and lack user-friendliness.To enhance the generalization and controllability of VTON generation, we propose Any2AnyTryon, which can generate try-on results based on different textual instructions and model garment images to meet various needs,eliminating the reliance on masks, poses, or other conditions. Specifically, we first construct the virtual try-on dataset LAION-Garment, the largest known open-source garment try-on dataset. Then, we introduce adaptive position embedding, which enables the model to generate satisfactory outfitted model images or garment images based on input images of different sizes and categories, significantly enhancing the generalization and controllability of VTON generation. In our experiments, we demonstrate the effectiveness of our Any2AnyTryon and compare it with existing methods. The results show that Any2AnyTryon enables flexible, controllable, and high-quality image-based virtual try-on generation.
|
2501.15891
|
13 pages,13 figures
| null | null |
[] |
[] |
[] |
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457
|
STEP-DETR: Advancing DETR-based Semi-Supervised Object Detection with Super Teacher and Pseudo-Label Guided Text Queries
|
[
"Tahira Shehzadi",
"Khurram Azeem Hashmi",
"Shalini Sarode",
"Didier Stricker",
"Muhammad Zeshan Afzal"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Shehzadi_STEP-DETR_Advancing_DETR-based_Semi-Supervised_Object_Detection_with_Super_Teacher_and_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Shehzadi_STEP-DETR_Advancing_DETR-based_Semi-Supervised_Object_Detection_with_Super_Teacher_and_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Shehzadi_STEP-DETR_Advancing_DETR-based_ICCV_2025_supplemental.pdf
|
@InProceedings{Shehzadi_2025_ICCV,
author = {Shehzadi, Tahira and Hashmi, Khurram Azeem and Sarode, Shalini and Stricker, Didier and Afzal, Muhammad Zeshan},
title = {STEP-DETR: Advancing DETR-based Semi-Supervised Object Detection with Super Teacher and Pseudo-Label Guided Text Queries},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {3069-3079}
}
|
This paper addresses key limitations in current Semi-Supervised Object Detection (SSOD) frameworks, focusing on issues related to pseudo-label quality, confidence bias, and inefficient query generation. Traditional methods, including CNN-based and DETR-based architectures, often face challenges such as noisy pseudo-labels, overfitting to common object categories, and consequently face difficulty detecting rare objects. Specifically, recent DETR-based SSOD approaches struggle with the one-to-many assignment strategy, which produces noisy pseudo-labels and overlapping predictions, resulting in suboptimal performance. To address these challenges, we propose STEP-DETR, a transformer-based SSOD framework. STEP-DETR introduces Super Teacher to generate higher-quality pseudo-labels and improve the student's learning process. Furthermore, STEP-DETR proposes Pseudo-Label Text Queries, which incorporate text embeddings from Super Teacher, balancing the student's confidence across common and rare categories, thereby mitigating confidence bias and enhancing generalization. Moreover, Denoising Text Guided Object Queries synthesizes query-label pairs for foreground and background using contrastive learning, enabling the model to better distinguish objects from background noise. To further boost performance and training efficiency, a Query Refinement Module is incorporated to filter out redundant denoising queries. On MS-COCO and Pascal VOC benchmarks, STEP-DETR outperforms state-of-the-art methods, demonstrating its effectiveness in improving semi-supervised object detection. Notably, with just 10% labeled data, it achieves 45.4 mAP, surpassing the baseline Semi-DETR by 1.9 mAP.
| null | null | null | null |
[] |
[] |
[] |
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] |
458
|
DISTA-Net: Dynamic Closely-Spaced Infrared Small Target Unmixing
|
[
"Shengdong Han",
"Shangdong Yang",
"Yuxuan Li",
"Xin Zhang",
"Xiang Li",
"Jian Yang",
"Ming-Ming Cheng",
"Yimian Dai"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Han_DISTA-Net_Dynamic_Closely-Spaced_Infrared_Small_Target_Unmixing_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Han_DISTA-Net_Dynamic_Closely-Spaced_Infrared_Small_Target_Unmixing_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Han_DISTA-Net_Dynamic_Closely-Spaced_ICCV_2025_supplemental.pdf
|
@InProceedings{Han_2025_ICCV,
author = {Han, Shengdong and Yang, Shangdong and Li, Yuxuan and Zhang, Xin and Li, Xiang and Yang, Jian and Cheng, Ming-Ming and Dai, Yimian},
title = {DISTA-Net: Dynamic Closely-Spaced Infrared Small Target Unmixing},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {14655-14664}
}
|
Resolving closely-spaced small targets in dense clusters presents a significant challenge in infrared imaging, as the overlapping signals hinder precise determination of their quantity, sub-pixel positions, and radiation intensities. While deep learning has advanced the field of infrared small target detection, its application to closely-spaced infrared small targets has not yet been explored. This gap exists primarily due to the complexity of separating superimposed characteristics and the lack of an open-source infrastructure. In this work, we propose the Dynamic Iterative Shrinkage Thresholding Network (DISTA-Net), which reconceptualizes traditional sparse reconstruction within a dynamic framework. DISTA-Net adaptively generates convolution weights and thresholding parameters to tailor the reconstruction process in real time. To the best of our knowledge, DISTA-Net is the first deep learning model designed specifically for the unmixing of closely-spaced infrared small targets, achieving superior sub-pixel detection accuracy. Moreover, we have established the first open-source ecosystem to foster further research in this field. This ecosystem comprises three key components: (1) CSIST-100K, a publicly available benchmark dataset; (2) CSO-mAP, a custom evaluation metric for sub-pixel detection; and (3) GrokCSO, an open-source toolkit featuring DISTA-Net and other state-of-the-art models, available at https://github.com/GrokCV/GrokCSO.
| null | null | null | null |
[] |
[] |
[] |
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] |
459
|
MDD: A Dataset for Text-and-Music Conditioned Duet Dance Generation
|
[
"Prerit Gupta",
"Jason Alexander Fotso-Puepi",
"Zhengyuan Li",
"Jay Mehta",
"Aniket Bera"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Gupta_MDD_A_Dataset_for_Text-and-Music_Conditioned_Duet_Dance_Generation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Gupta_MDD_A_Dataset_for_Text-and-Music_Conditioned_Duet_Dance_Generation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Gupta_MDD_A_Dataset_ICCV_2025_supplemental.zip
|
@InProceedings{Gupta_2025_ICCV,
author = {Gupta, Prerit and Fotso-Puepi, Jason Alexander and Li, Zhengyuan and Mehta, Jay and Bera, Aniket},
title = {MDD: A Dataset for Text-and-Music Conditioned Duet Dance Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13932-13941}
}
|
We introduce Multimodal DuetDance (MDD), a diverse multimodal benchmark dataset designed for text-controlled and music-conditioned 3D duet dance motion generation. Our dataset comprises 620 minutes of high-quality motion capture data performed by professional dancers, synchronized with music, and detailed with over 10K fine-grained natural language descriptions. The annotations capture a rich movement vocabulary, detailing spatial relationships, body movements, and rhythm, making MDD the first dataset to seamlessly integrate human motions, music, and text for duet dance generation. We introduce two novel tasks supported by our dataset: (1) Text-to-Duet, where given music and a textual prompt, both the leader and follower dance motion are generated (2) Text-to-Dance Accompaniment, where given music, textual prompt, and the leader's motion, the follower's motion is generated in a cohesive, text-aligned manner. We include baseline evaluations on both tasks to support future research. Please refer to the project website for the latest updates.
|
2508.16911
|
Accepted at ICCV 2025. Project page:
https://gprerit96.github.io/mdd-page
| null |
https://gprerit96.github.io/mdd-page/
|
[] |
[] |
[] |
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] |
460
|
Met2Net: A Decoupled Two-Stage Spatio-Temporal Forecasting Model for Complex Meteorological Systems
|
[
"Shaohan Li",
"Hao Yang",
"Min Chen",
"Xiaolin Qin"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_Met2Net_A_Decoupled_Two-Stage_Spatio-Temporal_Forecasting_Model_for_Complex_Meteorological_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_Met2Net_A_Decoupled_Two-Stage_Spatio-Temporal_Forecasting_Model_for_Complex_Meteorological_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_Met2Net_A_Decoupled_ICCV_2025_supplemental.pdf
|
@InProceedings{Li_2025_ICCV,
author = {Li, Shaohan and Yang, Hao and Chen, Min and Qin, Xiaolin},
title = {Met2Net: A Decoupled Two-Stage Spatio-Temporal Forecasting Model for Complex Meteorological Systems},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {5458-5468}
}
|
The increasing frequency of extreme weather events due to global climate change urges accurate weather prediction. Recently, great advances are made by the end-to-end methods, thanks to deep learning techniques, but they face limitations of representation inconsistency in multivariable integration and struggle to effectively capture the dependency between variables, which is required in complex weather systems. Treating different variables as distinct modalities and applying a two-stage training approach from multimodal models can partially alleviate this issue, but due to the inconformity in training tasks between the two stages, the results are often suboptimal. To address these challenges, we propose an implicit two-stage training method, configuring separate encoders and decoders for each variable. In detailed, in the first stage, the Translator is frozen while the Encoders and Decoders learn a shared latent space, in the second stage, the Encoders and Decoders are frozen, and the Translator captures inter-variable interactions for prediction. Besides, by introducing a self-attention mechanism for multivariable fusion in the latent space, the performance achieves further improvements. Empirically, extensive experiments shows state-of-the-art performance of our method. In specific, it reduces the MSE for near-surface air temperature and relative humidity predictions by 28.82% and 23.39%, respectively.
|
2507.17189
| null |
https://github.com/ShremG/Met2Net
| null |
[] |
[] |
[] |
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] |
461
|
SVG-Head: Hybrid Surface-Volumetric Gaussians for High-Fidelity Head Reconstruction and Real-Time Editing
|
[
"Heyi Sun",
"Cong Wang",
"Tian-Xing Xu",
"Jingwei Huang",
"Di Kang",
"Chunchao Guo",
"Song-Hai Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Sun_SVG-Head_Hybrid_Surface-Volumetric_Gaussians_for_High-Fidelity_Head_Reconstruction_and_Real-Time_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Sun_SVG-Head_Hybrid_Surface-Volumetric_Gaussians_for_High-Fidelity_Head_Reconstruction_and_Real-Time_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Sun_SVG-Head_Hybrid_Surface-Volumetric_ICCV_2025_supplemental.pdf
|
@InProceedings{Sun_2025_ICCV,
author = {Sun, Heyi and Wang, Cong and Xu, Tian-Xing and Huang, Jingwei and Kang, Di and Guo, Chunchao and Zhang, Song-Hai},
title = {SVG-Head: Hybrid Surface-Volumetric Gaussians for High-Fidelity Head Reconstruction and Real-Time Editing},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13326-13335}
}
|
Creating high-fidelity and editable head avatars is a pivotal challenge in computer vision and graphics, boosting many AR/VR applications. While recent advancements have achieved photorealistic renderings and plausible animation, head editing, especially real-time appearance editing, remains challenging due to the implicit representation and entangled modeling of the geometry and global appearance. To address this, we propose Surface-Volumetric Gaussian Head Avatar (SVG-Head), a novel hybrid representation that explicitly models the geometry with 3D Gaussians bound on a FLAME mesh and leverages disentangled texture images to capture the global appearance. Technically, it contains two types of Gaussians, in which surface Gaussians explicitly model the appearance of head avatars using learnable texture images, facilitating real-time texture editing, while volumetric Gaussians enhance the reconstruction quality of non-Lambertian regions (e.g., lips and hair). To model the correspondence between 3D world and texture space, we provide a mesh-aware Gaussian UV mapping method, which leverages UV coordinates given by the FLAME mesh to obtain sharp texture images and real-time rendering speed. A hierarchical optimization strategy is further designed to pursue the optimal performance in both reconstruction quality and editing flexibility. Experiments on the NeRSemble dataset show that SVG-Head not only generates high-fidelity rendering results, but also is the first method to obtain explicit texture images for Gaussian head avatars and support real-time appearance editing.
| null | null | null | null |
[] |
[] |
[] |
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] |
462
|
Dynamic Dictionary Learning for Remote Sensing Image Segmentation
|
[
"Xuechao Zou",
"Yue Li",
"Shun Zhang",
"Kai Li",
"Shiying Wang",
"Pin Tao",
"Junliang Xing",
"Congyan Lang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zou_Dynamic_Dictionary_Learning_for_Remote_Sensing_Image_Segmentation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zou_Dynamic_Dictionary_Learning_for_Remote_Sensing_Image_Segmentation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zou_Dynamic_Dictionary_Learning_ICCV_2025_supplemental.pdf
|
@InProceedings{Zou_2025_ICCV,
author = {Zou, Xuechao and Li, Yue and Zhang, Shun and Li, Kai and Wang, Shiying and Tao, Pin and Xing, Junliang and Lang, Congyan},
title = {Dynamic Dictionary Learning for Remote Sensing Image Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {22457-22466}
}
|
Remote sensing image segmentation faces persistent challenges in distinguishing morphologically similar categories and adapting to diverse scene variations. While existing methods rely on implicit representation learning paradigms, they often fail to dynamically adjust semantic embeddings according to contextual cues, leading to suboptimal performance in fine-grained scenarios such as cloud thickness differentiation. This work introduces a dynamic dictionary learning framework that explicitly models class ID embeddings through iterative refinement. The core contribution lies in a novel dictionary construction mechanism, where class-aware semantic embeddings are progressively updated via multi-stage alternating cross-attention querying between image features and dictionary embeddings. This process enables adaptive representation learning tailored to input-specific characteristics, effectively resolving ambiguities in intra-class heterogeneity and inter-class homogeneity. To further enhance discriminability, a contrastive constraint is applied to the dictionary space, ensuring compact intra-class distributions while maximizing inter-class separability. Extensive experiments across both coarse- and fine-grained datasets demonstrate consistent improvements over state-of-the-art methods, particularly in two online test benchmarks (LoveDA and UAVid). Code is available at https://github.com/XavierJiezou/D2LS.
|
2503.06683
| null | null |
https://xavierjiezou.github.io/D2LS/
|
[] |
[] |
[] |
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] |
463
|
Video Motion Graphs
|
[
"Haiyang Liu",
"Zhan Xu",
"Fa-Ting Hong",
"Hsin-Ping Huang",
"Yi Zhou",
"Yang Zhou"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_Video_Motion_Graphs_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_Video_Motion_Graphs_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_Video_Motion_Graphs_ICCV_2025_supplemental.pdf
|
@InProceedings{Liu_2025_ICCV,
author = {Liu, Haiyang and Xu, Zhan and Hong, Fa-Ting and Huang, Hsin-Ping and Zhou, Yi and Zhou, Yang},
title = {Video Motion Graphs},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13730-13740}
}
|
We present Video Motion Graphs, a system designed to generate realistic human motion videos. Using a reference video and conditional signals such as music or motion tags, the system synthesizes new videos by first retrieving video clips with gestures matching the conditions and then generating interpolation frames to seamlessly connect clip boundaries. The core of our approach is HMInterp, a robust Video Frame Interpolation (VFI) model that enables seamless interpolation of discontinuous frames, even for complex motion scenarios like dancing. HMInterp i) employs a dual-branch interpolation approach, combining a Motion Diffusion Model for human skeleton motion interpolation with a diffusion-based video frame interpolation model for final frame generation. ii) adopts condition progressive training to effectively leverage identity strong and weak conditions, such as images and pose. These designs ensure both high video texture quality and accurate motion trajectory. Our Video Motion Graphs outperforms existing generative- and retrieval-based methods for human motion video generation. Our codes and pretrained models are public available.
|
2503.20218
|
14 pages,10 figures
| null | null |
[] |
[] |
[] |
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] |
464
|
Ask and Remember: A Questions-Only Replay Strategy for Continual Visual Question Answering
|
[
"Imad Eddine Marouf",
"Enzo Tartaglione",
"Stéphane Lathuilière",
"Joost Van De Weijer"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Marouf_Ask_and_Remember_A_Questions-Only_Replay_Strategy_for_Continual_Visual_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Marouf_Ask_and_Remember_A_Questions-Only_Replay_Strategy_for_Continual_Visual_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Marouf_Ask_and_Remember_ICCV_2025_supplemental.pdf
|
@InProceedings{Marouf_2025_ICCV,
author = {Marouf, Imad Eddine and Tartaglione, Enzo and Lathuili\`ere, St\'ephane and Van De Weijer, Joost},
title = {Ask and Remember: A Questions-Only Replay Strategy for Continual Visual Question Answering},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {18078-18089}
}
|
Continual Learning in Visual Question Answering (VQACL) requires models to acquire new visual-linguistic skills (plasticity) while preserving previously learned knowledge (stability). The inherent multimodality of VQACL exacerbates this challenge, as models must balance stability across visual and textual domains while adapting to novel objects and reasoning tasks. Existing methods, primarily designed for unimodal settings, often fall short in addressing this dual requirement. In this work, we present QUestion-only replay with Attention Distillation (QUAD), a novel approach for VQACL that leverages only past task questions for regularization. By eliminating the need to store visual data, QUAD not only reduces memory overhead, but also alleviates privacy concerns. Our method introduces a Question-only Replay mechanism that selectively reuses prior task questions to counteract overfitting to the answer space of the current task, addressing the problem out of answer set. Complementing this, we propose Attention Consistency Distillation to enforce both intra-modal and inter-modal attention consistency across tasks, preserving essential visual-linguistic associations. Extensive experiments on VQAv2 and NExT-QA demonstrate that QUAD significantly outperforms state-of-the-art methods, achieving robust performance in continual VQA.
|
2502.04469
|
ICCV 2025, 8 pages. Code: https://github.com/IemProg/QUAD
|
https://github.com/IemProg/QUAD
| null |
[] |
[] |
[] |
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] |
465
|
Dense Policy: Bidirectional Autoregressive Learning of Actions
|
[
"Yue Su",
"Xinyu Zhan",
"Hongjie Fang",
"Han Xue",
"Hao-Shu Fang",
"Yong-Lu Li",
"Cewu Lu",
"Lixin Yang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Su_Dense_Policy_Bidirectional_Autoregressive_Learning_of_Actions_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Su_Dense_Policy_Bidirectional_Autoregressive_Learning_of_Actions_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Su_Dense_Policy_Bidirectional_ICCV_2025_supplemental.pdf
|
@InProceedings{Su_2025_ICCV,
author = {Su, Yue and Zhan, Xinyu and Fang, Hongjie and Xue, Han and Fang, Hao-Shu and Li, Yong-Lu and Lu, Cewu and Yang, Lixin},
title = {Dense Policy: Bidirectional Autoregressive Learning of Actions},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {14486-14495}
}
|
Mainstream visuomotor policies predominantly rely on generative models for holistic action prediction, while current autoregressive policies, predicting the next token or chunk, have shown suboptimal results. This motivates a search for more effective learning methods to unleash the potential of autoregressive policies for robotic manipulation. This paper introduces a bidirectionally expanded learning approach, termed Dense Policy, to establish a new paradigm for autoregressive policies in action prediction. It employs a lightweight encoder-only architecture to iteratively unfold the action sequence from an initial single frame into the target sequence in a coarse-to-fine manner with logarithmic-time inference. Extensive experiments validate that our dense policy has superior autoregressive learning capabilities and can surpass existing holistic generative policies. Our policy, example data, and training code will be publicly available upon publication.
|
2503.13217
| null | null | null |
[] |
[] |
[] |
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] |
466
|
Conditional Visual Autoregressive Modeling for Pathological Image Restoration
|
[
"Ziyi Liu",
"Zhe Xu",
"Jiabo Ma",
"Wenqiang Li",
"Ruixuan Wang",
"Bo Du",
"Hao Chen"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_Conditional_Visual_Autoregressive_Modeling_for_Pathological_Image_Restoration_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_Conditional_Visual_Autoregressive_Modeling_for_Pathological_Image_Restoration_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_Conditional_Visual_Autoregressive_ICCV_2025_supplemental.pdf
|
@InProceedings{Liu_2025_ICCV,
author = {Liu, Ziyi and Xu, Zhe and Ma, Jiabo and Li, Wenqiang and Wang, Ruixuan and Du, Bo and Chen, Hao},
title = {Conditional Visual Autoregressive Modeling for Pathological Image Restoration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {17828-17837}
}
|
Pathological image has been recognized as the gold standard for cancer diagnosis for more than a century. However, some internal regions of pathological images may inevitably exhibit various degradation issues, including low resolution, image blurring, and image noising, which will affect disease diagnosis, staging, and risk stratification.Existing pathological image restoration methods were mainly based on generative adversarial networks (GANs) to improve image quality, which are limited by the inherent instability and loss of structural details, often resulting in artifacts in the restored images.Large scale of whole slide images (WSIs) also makes it hard for efficient processing and restoration. To address these limitations, we propose a conditional visual autoregressive model (CVARPath) for next-scale token prediction, guided by the degraded tokens from the current scale. We introduce a novel framework that employs quantified encoders specifically designed for pathological image generation, which learns consistent sparse vocabulary tokens through self-supervised contrastive learning. Furthermore, our method efficiently compresses image patches into compact degraded sparse tokens at smaller scales and reconstructs high-quality large-scale whole slide images (WSIs). This is achieved using only an 8x8 vocabulary index for 256x256 images while maintaining minimal reconstruction loss. Experimental results demonstrate that our approach significantly enhances image quality, achieving an approximately 30% improvement in mean Frechet inception distance (FID) compared to popular conditional GANs and diffusion models across various degradation scenarios in pathological images.
| null | null | null | null |
[] |
[] |
[] |
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] |
467
|
BlueNeg: A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration
|
[
"Hanyuan Liu",
"Chengze Li",
"Minshan Xie",
"Zhenni Wang",
"Jiawen Liang",
"Chi-Sing Leung",
"Tien-Tsin Wong"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_BlueNeg_A_35mm_Negative_Film_Dataset_for_Restoring_Channel-Heterogeneous_Deterioration_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_BlueNeg_A_35mm_Negative_Film_Dataset_for_Restoring_Channel-Heterogeneous_Deterioration_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_BlueNeg_A_35mm_ICCV_2025_supplemental.pdf
|
@InProceedings{Liu_2025_ICCV,
author = {Liu, Hanyuan and Li, Chengze and Xie, Minshan and Wang, Zhenni and Liang, Jiawen and Leung, Chi-Sing and Wong, Tien-Tsin},
title = {BlueNeg: A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13119-13128}
}
|
While digitally acquired photographs have been dominating since around 2000, there remains a huge amount of legacy photographs being acquired by optical cameras and are stored in the form of film negatives. In this paper, we address the unique challenge of channel-heterogeneous deterioration in film negatives and introduce BlueNeg, the first high-quality 35mm negative film dataset specifically designed for restoration in this context. Our work aims to spotlight this underexplored area of image restoration on channel-heterogeneous deterioration. A key challenge in evaluating restoration performance is the absence of ground truth, as many negatives are already contaminated. To overcome this, we utilize printed photographs from the same negatives as proxies for quantitative assessment. These prints, while affected by spatially invariant color fading, are less affected from channel-heterogeneous degradation. We propose a reverse-printing process to estimate pseudo-ground truth from the prints and introduce a dedicated evaluation protocol. Our empirical analysis shows that existing restoration methods struggle with this dataset, highlighting the need for specialized techniques. We hope that BlueNeg and our evaluation framework will inspire further research in legacy photograph restoration, contributing to the digital preservation of historical moments for archival use. Our dataset is publicly available at https://huggingface.co/datasets/ttgroup/blueneg-release and https://blueneg.github.io
| null | null | null | null |
[] |
[] |
[] |
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] |
468
|
egoPPG: Heart Rate Estimation from Eye-Tracking Cameras in Egocentric Systems to Benefit Downstream Vision Tasks
|
[
"Björn Braun",
"Rayan Armani",
"Manuel Meier",
"Max Moebus",
"Christian Holz"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Braun_egoPPG_Heart_Rate_Estimation_from_Eye-Tracking_Cameras_in_Egocentric_Systems_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Braun_egoPPG_Heart_Rate_Estimation_from_Eye-Tracking_Cameras_in_Egocentric_Systems_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Braun_egoPPG_Heart_Rate_ICCV_2025_supplemental.pdf
|
@InProceedings{Braun_2025_ICCV,
author = {Braun, Bj\"orn and Armani, Rayan and Meier, Manuel and Moebus, Max and Holz, Christian},
title = {egoPPG: Heart Rate Estimation from Eye-Tracking Cameras in Egocentric Systems to Benefit Downstream Vision Tasks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {5579-5590}
}
|
Egocentric vision systems aim to understand the spatial surroundings and the wearer's behavior inside it, including motions, activities, and interactions. We argue that egocentric systems must additionally detect physiological states to capture a person's attention and situational responses, which are critical for context-aware behavior modeling. In this paper, we propose egoPPG, a novel vision task for egocentric systems to recover a person's cardiac activity to aid downstream vision tasks. We introduce PulseFormer, a method to extract heart rate as a key indicator of physiological state from the eye tracking cameras on unmodified egocentric vision systems. PulseFormer continuously estimates the photoplethysmogram (PPG) from areas around the eyes and fuses motion cues from the headset's inertial measurement unit to track HR values. We demonstrate egoPPG's downstream benefit for a key task on EgoExo4D, an existing egocentric dataset for which we find PulseFormer's estimates of HR to improve proficiency estimation by 14%. To train and validate PulseFormer, we collected a dataset of 13+ hours of eye tracking videos from Project Aria and contact-based PPG signals as well as an electrocardiogram (ECG) for ground-truth HR values. Similar to EgoExo4D, 25 participants performed diverse everyday activities such as office work, cooking, dancing, and exercising, which induced significant natural motion and HR variation (44-164 bpm). Our model robustly estimates HR (MAE=7.67 bpm) and captures patterns (r=0.85). Our results show how egocentric systems may unify environmental and physiological tracking to better understand users and that egoPPG as a complementary task provides meaningful augmentations for existing datasets and tasks. We release our code, dataset, and HR augmentations for EgoExo4D to inspire research on physiology-aware egocentric tasks.
|
2502.20879
|
Accepted for publication at ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
469
|
Beyond Pixel Uncertainty: Bounding the OoD Objects in Road Scenes
|
[
"Huachao Zhu",
"Zelong Liu",
"Zhichao Sun",
"Yuda Zou",
"Gui-Song Xia",
"Yongchao Xu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhu_Beyond_Pixel_Uncertainty_Bounding_the_OoD_Objects_in_Road_Scenes_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhu_Beyond_Pixel_Uncertainty_Bounding_the_OoD_Objects_in_Road_Scenes_ICCV_2025_paper.pdf
| null |
@InProceedings{Zhu_2025_ICCV,
author = {Zhu, Huachao and Liu, Zelong and Sun, Zhichao and Zou, Yuda and Xia, Gui-Song and Xu, Yongchao},
title = {Beyond Pixel Uncertainty: Bounding the OoD Objects in Road Scenes},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {8472-8481}
}
|
Recognizing out-of-distribution (OoD) objects on roads is crucial for safe driving. Most existing methods rely on segmentation models' uncertainty as anomaly scores, often resulting in false positives - especially at ambiguous regions like boundaries, where segmentation models inherently exhibit high uncertainty. Additionally, it is challenging to define a suitable threshold to generate anomaly masks, especially with the inconsistencies in predictions across consecutive frames. We propose DetSeg, a novel paradigm that helps incorporate object-level understanding. DetSeg first detects all objects in the open world and then suppresses in-distribution (ID) bounding boxes, leaving only OoD proposals. These proposals can either help previous methods eliminate false positives (DetSeg-\mathcal R ), or generate binary anomaly masks without complex threshold search when combined with a box-prompted segmentation module (DetSeg-\mathcal S ). Additionally, we introduce vanishing point guided Hungarian matching (VPHM) to smooth the prediction results within a video clip, mitigating abrupt variations of predictions between consecutive frames. Comprehensive experiments on various benchmarks demonstrate that DetSeg significantly improves performance, reducing the FPR\it _ 95 of previous methods by up to 37.45%, offering a more robust and practical solution. Code: https://github.com/huachao0124/DetSeg-official.
| null | null | null | null |
[] |
[] |
[] |
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] |
470
|
Gain-MLP: Improving HDR Gain Map Encoding via a Lightweight MLP
|
[
"Trevor D. Canham",
"SaiKiran Tedla",
"Michael J. Murdoch",
"Michael S. Brown"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Canham_Gain-MLP_Improving_HDR_Gain_Map_Encoding_via_a_Lightweight_MLP_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Canham_Gain-MLP_Improving_HDR_Gain_Map_Encoding_via_a_Lightweight_MLP_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Canham_Gain-MLP_Improving_HDR_ICCV_2025_supplemental.pdf
|
@InProceedings{Canham_2025_ICCV,
author = {Canham, Trevor D. and Tedla, SaiKiran and Murdoch, Michael J. and Brown, Michael S.},
title = {Gain-MLP: Improving HDR Gain Map Encoding via a Lightweight MLP},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {18619-18628}
}
|
While most images shared on the web and social media platforms are encoded in standard dynamic range (SDR), many displays now can accommodate high dynamic range (HDR) content. Additionally, modern cameras can capture images in an HDR format but convert them to SDR to ensure maximum compatibility with existing workflows and legacy displays. To support both SDR and HDR, new encoding formats are emerging that store additional metadata in SDR images in the form of a gain map. When applied to the SDR image, the gain map recovers the HDR version of the image as needed. These gain maps, however, are typically down-sampled and encoded using standard image compression, such as JPEG and HEIC, which can result in unwanted artifacts. In this paper, we propose to use a lightweight multi-layer perceptron (MLP) network to encode the gain map. The MLP is optimized using the SDR image information as input and provides superior performance in terms of HDR reconstruction. Moreover, the MLP-based approach uses a fixed memory footprint (10 KB) and requires no additional adjustments to accommodate different image sizes or encoding parameters. We conduct extensive experiments on various MLP based HDR embedding strategies and demonstrate that our approach outperforms the current state-of-the-art.
|
2503.11883
| null | null | null |
[] |
[] |
[] |
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] |
471
|
MaterialMVP: Illumination-Invariant Material Generation via Multi-view PBR Diffusion
|
[
"Zebin He",
"Mingxin Yang",
"Shuhui Yang",
"Yixuan Tang",
"Tao Wang",
"Kaihao Zhang",
"Guanying Chen",
"Yuhong Liu",
"Jie Jiang",
"Chunchao Guo",
"Wenhan Luo"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/He_MaterialMVP_Illumination-Invariant_Material_Generation_via_Multi-view_PBR_Diffusion_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/He_MaterialMVP_Illumination-Invariant_Material_Generation_via_Multi-view_PBR_Diffusion_ICCV_2025_paper.pdf
| null |
@InProceedings{He_2025_ICCV,
author = {He, Zebin and Yang, Mingxin and Yang, Shuhui and Tang, Yixuan and Wang, Tao and Zhang, Kaihao and Chen, Guanying and Liu, Yuhong and Jiang, Jie and Guo, Chunchao and Luo, Wenhan},
title = {MaterialMVP: Illumination-Invariant Material Generation via Multi-view PBR Diffusion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26294-26305}
}
|
Physically-based rendering (PBR) has become a cornerstone in modern computer graphics, enabling realistic material representation and lighting interactions in 3D scenes. In this paper, we present MaterialMVP, a novel end-to-end model for generating PBR textures from 3D meshes and image prompts, addressing key challenges in multi-view material synthesis. Our approach leverages Reference Attention to extract and encode informative latent from the input reference images, enabling intuitive and controllable texture generation. We also introduce a Consistency-Regularized Training strategy to enforce stability across varying viewpoints and illumination conditions, ensuring illumination-invariant and geometrically consistent results. Additionally, we propose Dual-Channel Material Generation, which separately optimizes albedo and metallic-roughness (MR) textures while maintaining precise spatial alignment with the input images through Multi-Channel Aligned Attention. Learnable material embeddings are further integrated to capture the distinct properties of albedo and MR. Experimental results demonstrate that our model generates PBR textures with realistic behavior across diverse lighting scenarios, outperforming existing methods in both consistency and quality for scalable 3D asset creation.
|
2503.10289
| null | null |
https://zebinhe.github.io/MaterialMVP/
|
[] |
[] |
[] |
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] |
472
|
Learning to See in the Extremely Dark
|
[
"Hai Jiang",
"Binhao Guan",
"Zhen Liu",
"Xiaohong Liu",
"Jian Yu",
"Zheng Liu",
"Songchen Han",
"Shuaicheng Liu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Jiang_Learning_to_See_in_the_Extremely_Dark_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Jiang_Learning_to_See_in_the_Extremely_Dark_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Jiang_Learning_to_See_ICCV_2025_supplemental.pdf
|
@InProceedings{Jiang_2025_ICCV,
author = {Jiang, Hai and Guan, Binhao and Liu, Zhen and Liu, Xiaohong and Yu, Jian and Liu, Zheng and Han, Songchen and Liu, Shuaicheng},
title = {Learning to See in the Extremely Dark},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {7676-7685}
}
|
Learning-based methods have made promising advances in low-light RAW image enhancement, while their capability to extremely dark scenes where the environmental illuminance drops as low as 0.0001 lux remains to be explored due to the lack of corresponding datasets. To this end, we propose a paired-to-paired data synthesis pipeline capable of generating well-calibrated extremely low-light RAW images at three precise illuminance ranges of 0.01-0.1 lux, 0.001-0.01 lux, and 0.0001-0.001 lux, together with high-quality sRGB references to comprise a large-scale paired dataset named See-in-the-Extremely-Dark (SIED) to benchmark low-light RAW image enhancement approaches. Furthermore, we propose a diffusion-based framework that leverages the generative ability and intrinsic denoising property of diffusion models to restore visually pleasing results from extremely low-SNR RAW inputs, in which an Adaptive Illumination Correction Module (AICM) and a color consistency loss are introduced to ensure accurate exposure correction and color restoration. Extensive experiments on the proposed SIED and publicly available benchmarks demonstrate the effectiveness of our method. The code and dataset are available at https://github.com/JianghaiSCU/SIED.
|
2506.21132
|
Accepted by ICCV 2025
|
https://github.com/JianghaiSCU/SIED
| null |
[] |
[] |
[] |
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] |
473
|
StyleMotif: Multi-Modal Motion Stylization using Style-Content Cross Fusion
|
[
"Ziyu Guo",
"Young Yoon Lee",
"Joseph Liu",
"Yizhak Ben-Shabat",
"Victor Zordan",
"Mubbasir Kapadia"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Guo_StyleMotif_Multi-Modal_Motion_Stylization_using_Style-Content_Cross_Fusion_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Guo_StyleMotif_Multi-Modal_Motion_Stylization_using_Style-Content_Cross_Fusion_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Guo_StyleMotif_Multi-Modal_Motion_ICCV_2025_supplemental.pdf
|
@InProceedings{Guo_2025_ICCV,
author = {Guo, Ziyu and Lee, Young Yoon and Liu, Joseph and Ben-Shabat, Yizhak and Zordan, Victor and Kapadia, Mubbasir},
title = {StyleMotif: Multi-Modal Motion Stylization using Style-Content Cross Fusion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13349-13359}
}
|
We present StyleMotif, a novel Stylized Motion Latent Diffusion model, generating motion conditioned on both content and style from multiple modalities. Unlike existing approaches that either focus on generating diverse motion content or transferring style from sequences, StyleMotif seamlessly synthesizes motion across a wide range of content while incorporating stylistic cues from multi-modal inputs, including motion, text, image, video, and audio. To achieve this, we introduce a style-content cross fusion mechanism and align a style encoder with a pre-trained multi-modal model, ensuring that the generated motion accurately captures the reference style while preserving realism. Extensive experiments demonstrate that our framework surpasses existing methods in stylized motion generation and exhibits emergent capabilities for multi-modal motion stylization, enabling more nuanced motion synthesis. Project Page: https://stylemotif.github.io.
|
2503.21775
|
Project Page: https://stylemotif.github.io
| null | null |
[] |
[] |
[] |
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] |
474
|
SpecGuard: Spectral Projection-based Advanced Invisible Watermarking
|
[
"Inzamamul Alam",
"Md Tanvir Islam",
"Simon S. Woo",
"Khan Muhammad"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Alam_SpecGuard_Spectral_Projection-based_Advanced_Invisible_Watermarking_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Alam_SpecGuard_Spectral_Projection-based_Advanced_Invisible_Watermarking_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Alam_SpecGuard_Spectral_Projection-based_ICCV_2025_supplemental.pdf
|
@InProceedings{Alam_2025_ICCV,
author = {Alam, Inzamamul and Islam, Md Tanvir and Woo, Simon S. and Muhammad, Khan},
title = {SpecGuard: Spectral Projection-based Advanced Invisible Watermarking},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {17984-17993}
}
|
Watermarking embeds imperceptible patterns into images for authenticity verification. However, existing methods often lack robustness against various transformations primarily including distortions, image regeneration, and adversarial perturbation, creating real-world challenges. In this work, we introduce SpecGuard, a novel watermarking approach for robust and invisible image watermarking. Unlike prior approaches, we embed the message inside hidden convolution layers by converting from the spatial domain to the frequency domain using spectral projection of a higher frequency band that is decomposed by wavelet projection. Spectral projection employs Fast Fourier Transform approximation to transform spatial data into the frequency domain efficiently. In the encoding phase, a strength factor enhances resilience against diverse attacks, including adversarial, geometric, and regeneration-based distortions, ensuring the preservation of copyrighted information. Meanwhile, the decoder leverages Parseval's theorem to effectively learn and extract the watermark pattern, enabling accurate retrieval under challenging transformations. We evaluate the proposed SpecGuard based on the embedded watermark's invisibility, capacity, and robustness. Comprehensive experiments demonstrate the proposed SpecGuard outperforms the state-of-the-art models. To ensure reproducibility, the full code is released on \href https://github.com/inzamamulDU/SpecGuard_ICCV_2025 GitHub .
|
2510.07302
| null | null | null |
[] |
[] |
[] |
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] |
475
|
Scaling Laws for Native Multimodal Models
|
[
"Mustafa Shukor",
"Enrico Fini",
"Victor Guilherme Turrisi da Costa",
"Matthieu Cord",
"Joshua Susskind",
"Alaaeldin El-Nouby"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Shukor_Scaling_Laws_for_Native_Multimodal_Models_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Shukor_Scaling_Laws_for_Native_Multimodal_Models_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Shukor_Scaling_Laws_for_ICCV_2025_supplemental.pdf
|
@InProceedings{Shukor_2025_ICCV,
author = {Shukor, Mustafa and Fini, Enrico and da Costa, Victor Guilherme Turrisi and Cord, Matthieu and Susskind, Joshua and El-Nouby, Alaaeldin},
title = {Scaling Laws for Native Multimodal Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {12-23}
}
|
Building general-purpose models that can effectively perceive the world through multimodal signals has been a long-standing goal. Current approaches involve integrating separately pre-trained components, such as connecting vision encoders to LLMs and continuing multimodal training. While such approaches exhibit remarkable sample efficiency, it remains an open question whether such late-fusion architectures are inherently superior. In this work, we revisit the architectural design of native multimodal models (NMMs)--those trained from the ground up on all modalities--and conduct an extensive scaling laws study, spanning 457 trained models with different architectures and training mixtures. Our investigation reveals no inherent advantage to late-fusion architectures over early-fusion ones, which do not rely on image encoders or tokenizers. On the contrary, early fusion exhibits stronger performance at lower parameter counts, is more efficient to train, and is easier to deploy. Motivated by the strong performance of the early-fusion architectures, we show that incorporating Mixture of Experts (MoEs) allows models to learn modality-specific weights, significantly benefiting performance.
|
2504.07951
|
ICCV 2025 (Oral). 28 figures, 13 tables
| null | null |
[] |
[] |
[] |
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] |
476
|
Bridging Diffusion Models and 3D Representations: A 3D Consistent Super-Resolution Framework
|
[
"Yi-Ting Chen",
"Ting-Hsuan Liao",
"Pengsheng Guo",
"Alexander Schwing",
"Jia-Bin Huang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_Bridging_Diffusion_Models_and_3D_Representations_A_3D_Consistent_Super-Resolution_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_Bridging_Diffusion_Models_and_3D_Representations_A_3D_Consistent_Super-Resolution_ICCV_2025_paper.pdf
| null |
@InProceedings{Chen_2025_ICCV,
author = {Chen, Yi-Ting and Liao, Ting-Hsuan and Guo, Pengsheng and Schwing, Alexander and Huang, Jia-Bin},
title = {Bridging Diffusion Models and 3D Representations: A 3D Consistent Super-Resolution Framework},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13481-13490}
}
|
We propose 3D Super Resolution (3DSR), a novel 3D Gaussian-splatting-based super-resolution framework that leverages off-the-shelf diffusion-based 2D super-resolution models. 3DSR encourages 3D consistency across views via the use of an explicit 3D Gaussian-splatting-based scene representation. This makes the proposed 3DSR different from prior work, such as image upsampling or the use of video super-resolution, which either don't consider 3D consistency or aim to incorporate 3D consistency implicitly. Notably, our method enhances visual quality without additional fine-tuning, ensuring spatial coherence within the reconstructed scene. We evaluate 3DSR on MipNeRF360 and LLFF data, demonstrating that it produces high-resolution results that are visually compelling, while maintaining structural consistency in 3D reconstructions. Code will be released.
|
2508.04090
|
Accepted to ICCV 2025
| null | null |
[] |
[] |
[] |
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477
|
MagicID: Hybrid Preference Optimization for ID-Consistent and Dynamic-Preserved Video Customization
|
[
"Hengjia Li",
"Lifan Jiang",
"Xi Xiao",
"Tianyang Wang",
"Hongwei Yi",
"Boxi Wu",
"Deng Cai"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_MagicID_Hybrid_Preference_Optimization_for_ID-Consistent_and_Dynamic-Preserved_Video_Customization_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_MagicID_Hybrid_Preference_Optimization_for_ID-Consistent_and_Dynamic-Preserved_Video_Customization_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_MagicID_Hybrid_Preference_ICCV_2025_supplemental.zip
|
@InProceedings{Li_2025_ICCV,
author = {Li, Hengjia and Jiang, Lifan and Xiao, Xi and Wang, Tianyang and Yi, Hongwei and Wu, Boxi and Cai, Deng},
title = {MagicID: Hybrid Preference Optimization for ID-Consistent and Dynamic-Preserved Video Customization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {12737-12746}
}
|
Video identity customization seeks to produce high-fidelity videos that maintain consistent identity and exhibit significant dynamics based on users' reference images. However, existing approaches face two key challenges: identity degradation over extended video length and reduced dynamics during training, primarily due to their reliance on traditional self-reconstruction training with static images. To address these issues, we introduce MagicID, a novel framework designed to directly promote the generation of identity-consistent and dynamically rich videos tailored to user preferences. Specifically, we propose constructing pairwise preference video data with explicit identity and dynamic rewards for preference learning, instead of sticking to the traditional self-reconstruction. To address the constraints of customized preference data, we introduce a hybrid sampling strategy. This approach first prioritizes identity preservation by leveraging static videos derived from reference images, then enhances dynamic motion quality in the generated videos using a Frontier-based sampling method. By utilizing these hybrid preference pairs, we optimize the model to align with the reward differences between pairs of customized preferences. Extensive experiments show that MagicID successfully achieves consistent identity and natural dynamics, surpassing existing methods across various metrics.
|
2503.12689
| null | null | null |
[] |
[] |
[] |
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] |
478
|
LookOut: Real-World Humanoid Egocentric Navigation
|
[
"Boxiao Pan",
"Adam W. Harley",
"Francis Engelmann",
"C. Karen Liu",
"Leonidas J. Guibas"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Pan_LookOut_Real-World_Humanoid_Egocentric_Navigation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Pan_LookOut_Real-World_Humanoid_Egocentric_Navigation_ICCV_2025_paper.pdf
| null |
@InProceedings{Pan_2025_ICCV,
author = {Pan, Boxiao and Harley, Adam W. and Engelmann, Francis and Liu, C. Karen and Guibas, Leonidas J.},
title = {LookOut: Real-World Humanoid Egocentric Navigation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24977-24988}
}
|
The ability to predict collision-free future trajectories from egocentric observations is crucial in applications such as humanoid robotics, VR / AR, and assistive navigation. In this work, we introduce the challenging problem of predicting a sequence of future 6D head poses from an egocentric video. In particular, we predict both head translations and rotations to learn the active information-gathering behavior expressed through head-turning events. To solve this task, we propose a framework that reasons over temporally aggregated 3D latent features, which models the geometric and semantic constraints for both the static and dynamic parts of the environment. Motivated by the lack of training data in this space, we further contribute a data collection pipeline using the Project Aria glasses, and present a dataset collected through this approach. Our dataset, dubbed Aria Navigation Dataset (AND), consists of 4 hours of recording of users navigating in real-world scenarios. It includes diverse situations and navigation behaviors, providing a valuable resource for learning real-world egocentric navigation policies. Extensive experiments show that our model learns human-like navigation behaviors such as waiting / slowing down, rerouting, and looking around for traffic while generalizing to unseen environments. Check out our project webpage at https://sites.google.com/stanford.edu/lookout.
|
2508.14466
| null | null | null |
[] |
[] |
[] |
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] |
479
|
Lightweight Gradient-Aware Upscaling of 3D Gaussian Splatting Images
|
[
"Simon Niedermayr",
"Christoph Neuhauser",
"Rüdiger Westermann"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Niedermayr_Lightweight_Gradient-Aware_Upscaling_of_3D_Gaussian_Splatting_Images_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Niedermayr_Lightweight_Gradient-Aware_Upscaling_of_3D_Gaussian_Splatting_Images_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Niedermayr_Lightweight_Gradient-Aware_Upscaling_ICCV_2025_supplemental.pdf
|
@InProceedings{Niedermayr_2025_ICCV,
author = {Niedermayr, Simon and Neuhauser, Christoph and Westermann, R\"udiger},
title = {Lightweight Gradient-Aware Upscaling of 3D Gaussian Splatting Images},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {25862-25871}
}
|
We introduce an image upscaling technique tailored for 3D Gaussian Splatting (3DGS) on lightweight GPUs. Compared to 3DGS, it achieves significantly higher rendering speeds and reduces artifacts commonly observed in 3DGS reconstructions. Our technique upscales low-resolution 3DGS renderings with a marginal increase in cost by directly leveraging the analytical image gradients of Gaussians for gradient-based bicubic spline interpolation.The technique is agnostic to the specific 3DGS implementation, achieving novel view synthesis at rates 3x-4x higher than the baseline implementation.Through extensive experiments on multiple datasets, we showcase the performance improvements and high reconstruction fidelity attainable with gradient-aware upscaling of 3DGS images.We further demonstrate the integration of gradient-aware upscaling into the gradient-based optimization of a 3DGS model and analyze its effects on reconstruction quality and performance.
|
2503.14171
| null | null | null |
[] |
[] |
[] |
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] |
480
|
Motion-2-to-3: Leveraging 2D Motion Data for 3D Motion Generations
|
[
"Ruoxi Guo",
"Huaijin Pi",
"Zehong Shen",
"Qing Shuai",
"Zechen Hu",
"Zhumei Wang",
"Yajiao Dong",
"Ruizhen Hu",
"Taku Komura",
"Sida Peng",
"Xiaowei Zhou"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Guo_Motion-2-to-3_Leveraging_2D_Motion_Data_for_3D_Motion_Generations_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Guo_Motion-2-to-3_Leveraging_2D_Motion_Data_for_3D_Motion_Generations_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Guo_Motion-2-to-3_Leveraging_2D_ICCV_2025_supplemental.zip
|
@InProceedings{Guo_2025_ICCV,
author = {Guo, Ruoxi and Pi, Huaijin and Shen, Zehong and Shuai, Qing and Hu, Zechen and Wang, Zhumei and Dong, Yajiao and Hu, Ruizhen and Komura, Taku and Peng, Sida and Zhou, Xiaowei},
title = {Motion-2-to-3: Leveraging 2D Motion Data for 3D Motion Generations},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {14305-14316}
}
|
Text-driven human motion synthesis has showcased its potential for revolutionizing motion design in the movie and game industry.Existing methods often rely on 3D motion capture data, which requires special setups, resulting in high costs for data acquisition, ultimately limiting the diversity and scope of human motion. In contrast, 2D human videos offer a vast and accessible source of motion data, covering a wider range of styles and activities.In this paper, we explore the use of 2D human motion extracted from videos as an alternative data source to improve text-driven 3D motion generation.Our approach introduces a novel framework that disentangles local joint motion from global movements, enabling efficient learning of local motion priors from 2D data.We first train a single-view 2D local motion generator on a large dataset of text-2D motion pairs.Then we fine-tune the generator with 3D data, transforming it into a multi-view generator that predicts view-consistent local joint motion and root dynamics.Evaluations on the well-acknowledged datasets and novel text prompts demonstrate that our method can efficiently utilizes 2D data, supporting a wider range of realistic 3D human motion generation.
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] |
481
|
Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation
|
[
"Tianyu Zou",
"Shengwu Xiong",
"Ruilin Yao",
"Yi Rong"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zou_Balancing_Conservatism_and_Aggressiveness_Prototype-Affinity_Hybrid_Network_for_Few-Shot_Segmentation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zou_Balancing_Conservatism_and_Aggressiveness_Prototype-Affinity_Hybrid_Network_for_Few-Shot_Segmentation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zou_Balancing_Conservatism_and_ICCV_2025_supplemental.pdf
|
@InProceedings{Zou_2025_ICCV,
author = {Zou, Tianyu and Xiong, Shengwu and Yao, Ruilin and Rong, Yi},
title = {Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {20561-20571}
}
|
This paper studies the few-shot segmentation (FSS) task, which aims to segment objects belonging to unseen categories in a query image by learning a model on a small number of well-annotated support samples. Our analysis of two mainstream FSS paradigms reveals that the predictions made by prototype learning methods are usually conservative, while those of affinity learning methods tend to be more aggressive. This observation motivates us to balance the conservative and aggressive information captured by these two types of FSS frameworks so as to improve the segmentation performance. To achieve this, we propose a Prototype-Affinity Hybrid Network (PAHNet), which introduces a Prototype-guided Feature Enhancement (PFE) module and an Attention Score Calibration (ASC) module in each attention block of an affinity learning model (called affinity learner). These two modules utilize the predictions generated by a pre-trained prototype learning model (called prototype predictor) to enhance the foreground information in support and query image representations and suppress the mismatched foreground-background (FG-BG) relationships between them, respectively. In this way, the aggressiveness of the affinity learner can be effectively mitigated, thereby eventually increasing the segmentation accuracy of our PAHNet method. Experimental results show that PAHNet outperforms most recently proposed methods across 1-shot and 5-shot settings on both PASCAL-5^i and COCO-20^i datasets, suggesting its effectiveness. The code is available at: https://github.com/tianyu-zou/PAHNet.
|
2507.19140
|
8 pages, 7 figures
|
https://github.com/tianyu-zou/PAHNet
| null |
[] |
[] |
[] |
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] |
482
|
Large Scene Generation with Cube-Absorb Discrete Diffusion
|
[
"Qianjiang Hu",
"Wei Hu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Hu_Large_Scene_Generation_with_Cube-Absorb_Discrete_Diffusion_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Hu_Large_Scene_Generation_with_Cube-Absorb_Discrete_Diffusion_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Hu_Large_Scene_Generation_ICCV_2025_supplemental.pdf
|
@InProceedings{Hu_2025_ICCV,
author = {Hu, Qianjiang and Hu, Wei},
title = {Large Scene Generation with Cube-Absorb Discrete Diffusion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {25186-25196}
}
|
Generating realistic 3D outdoor scenes is essential for applications in autonomous driving, virtual reality, environmental science, and urban development. Traditional 3D generation approaches using single-layer diffusion methods can produce detailed scenes for individual objects but struggle with high-resolution, large-scale outdoor environments due to scalability limitations. Recent hierarchical diffusion models tackle this by progressively scaling up low-resolution scenes. However, they often sample fine details from pure noise rather than from the coarse scene, which limits the efficiency. We propose a novel cube-absorb discrete diffusion (CADD) model, which employs low-resolution scenes as the base state in the diffusion process to generate fine details, eliminating the need to sample entirely from noise. Moreover, we introduce the Sparse Cube Diffusion Transformer (SCDT), a transformer-based model with a sparse cube attention operator, optimized for generating large-scale sparse voxel scenes. Our method demonstrates state-of-the-art performance on the CarlaSC and KITTI360 datasets, supported by qualitative visualizations and extensive ablation studies that highlight the impact of the CADD process and sparse cube attention operator on high-resolution 3D scene generation.
| null | null | null | null |
[] |
[] |
[] |
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] |
483
|
Advancing Text-to-3D Generation with Linearized Lookahead Variational Score Distillation
|
[
"Yu Lei",
"Bingde Liu",
"Qingsong Xie",
"Haonan Lu",
"Zhijie Deng"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Lei_Advancing_Text-to-3D_Generation_with_Linearized_Lookahead_Variational_Score_Distillation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Lei_Advancing_Text-to-3D_Generation_with_Linearized_Lookahead_Variational_Score_Distillation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Lei_Advancing_Text-to-3D_Generation_ICCV_2025_supplemental.pdf
|
@InProceedings{Lei_2025_ICCV,
author = {Lei, Yu and Liu, Bingde and Xie, Qingsong and Lu, Haonan and Deng, Zhijie},
title = {Advancing Text-to-3D Generation with Linearized Lookahead Variational Score Distillation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {19567-19576}
}
|
Text-to-3D generation based on score distillation of pre-trained 2D diffusion models has gained increasing interest, with variational score distillation (VSD) as a remarkable example. VSD proves that vanilla score distillation can be improved by introducing an extra score-based model, which characterizes the distribution of images rendered from 3D models, to correct the distillation gradient. Despite the theoretical foundations, VSD, in practice, is likely to suffer from slow and sometimes ill-posed convergence.In this paper, we perform an in-depth investigation of the interplay between the introduced score model and the 3D model, and find that there exists a mismatching problem between LoRA and 3D distributions in practical implementation. We can simply adjust their optimization order to improve the generation quality. By doing so, the score model looks ahead to the current 3D state and hence yields more reasonable corrections. Nevertheless, naive lookahead VSD may suffer from unstable training in practice due to the potential over-fitting. To address this, we propose to use a linearized variant of the model for score distillation, giving rise to the Linearized Lookahead Variational Score Distillation (L^2-VSD). L^2-VSD can be realized efficiently with forward-mode autodiff functionalities of existing deep learning libraries. Extensive experiments validate the efficacy of L^2-VSD, revealing its clear superiority over prior score distillation-based methods. We also show that our method can be seamlessly incorporated into any other VSD-based text-to-3D framework.
|
2507.09748
|
Accepted by ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
484
|
MS3D: High-Quality 3D Generation via Multi-Scale Representation Modeling
|
[
"Guan Luo",
"Jianfeng Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Luo_MS3D_High-Quality_3D_Generation_via_Multi-Scale_Representation_Modeling_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Luo_MS3D_High-Quality_3D_Generation_via_Multi-Scale_Representation_Modeling_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Luo_MS3D_High-Quality_3D_ICCV_2025_supplemental.zip
|
@InProceedings{Luo_2025_ICCV,
author = {Luo, Guan and Zhang, Jianfeng},
title = {MS3D: High-Quality 3D Generation via Multi-Scale Representation Modeling},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26336-26348}
}
|
High-quality textured mesh reconstruction from sparse-view images remains a fundamental challenge in computer graphics and computer vision. Traditional large reconstruction models operate in a single-scale manner, forcing the models to simultaneously capture global structure and local details, often resulting in compromised reconstructed shapes. In this work, we propose MS3D, a novel multi-scale 3D reconstruction framework. At its core, our method introduces a hierarchical structured latent representation for multi-scale modeling, coupled with a multi-scale feature extraction and integration mechanism. This enables progressive reconstruction, effectively decomposing the complex task of detailed geometry reconstruction into a sequence of easier steps. This coarse-to-fine approach effectively captures multi-frequency details, learns complex geometric patterns, and generalizes well across diverse objects while preserving fine-grained details. Extensive experiments demonstrate MS3D outperforms state-of-the-art methods and is broadly applicable to both image- and text-to-3D generation. The entire pipeline reconstructs high-quality textured meshes in under five seconds.
| null | null | null | null |
[] |
[] |
[] |
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] |
485
|
Memory-Efficient 4-bit Preconditioned Stochastic Optimization
|
[
"Jingyang Li",
"Kuangyu Ding",
"Kim-Chuan Toh",
"Pan Zhou"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_Memory-Efficient_4-bit_Preconditioned_Stochastic_Optimization_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_Memory-Efficient_4-bit_Preconditioned_Stochastic_Optimization_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_Memory-Efficient_4-bit_Preconditioned_ICCV_2025_supplemental.pdf
|
@InProceedings{Li_2025_ICCV,
author = {Li, Jingyang and Ding, Kuangyu and Toh, Kim-Chuan and Zhou, Pan},
title = {Memory-Efficient 4-bit Preconditioned Stochastic Optimization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {22633-22643}
}
|
Preconditioned stochastic optimization algorithms, exemplified by Shampoo, outperform first-order optimizers by offering theoretical convergence benefits and practical gains in large-scale neural network training. However, they incur substantial memory overhead due to the storage demands of non-diagonal preconditioning matrices. To address this, we introduce 4-bit quantization for Shampoo's preconditioners. We introduce two key methods: First, we apply Cholesky decomposition followed by quantization of the Cholesky factors, reducing memory usage by leveraging their lower triangular structure while better preserving spectral properties to minimize information loss. To our knowledge, this is the first quantization approach applied to Cholesky factors of preconditioners. Second, we incorporate error feedback in the quantization process, efficiently storing Cholesky factor and error state in the lower and upper triangular parts of the same matrix. Through extensive experiments, we demonstrate that combining Cholesky quantization with error feedback enhances memory efficiency and algorithm performance in large-scale deep-learning tasks. Theoretically, we also provide convergence proofs for quantized Shampoo under both smooth and non-smooth stochastic optimization settings.
| null | null | null | null |
[] |
[] |
[] |
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] |
486
|
On the Recovery of Cameras from Fundamental Matrices
|
[
"Rakshith Madhavan",
"Federica Arrigoni"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Madhavan_On_the_Recovery_of_Cameras_from_Fundamental_Matrices_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Madhavan_On_the_Recovery_of_Cameras_from_Fundamental_Matrices_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Madhavan_On_the_Recovery_ICCV_2025_supplemental.pdf
|
@InProceedings{Madhavan_2025_ICCV,
author = {Madhavan, Rakshith and Arrigoni, Federica},
title = {On the Recovery of Cameras from Fundamental Matrices},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {20934-20943}
}
|
The viewing graph is a compact tool to encode the geometry of multiple views: nodes represent uncalibrated cameras and edges represent fundamental matrices (when available). Most research focuses on theoretical analyses, exploring for which viewing graphs it is possible (in principle) to retrieve cameras from fundamental matrices, in the sense that the problem admits a unique solution for noiseless data. However, the practical task of recovering cameras from noisy fundamental matrices is still open, as available methods are limited to special graphs (such as those covered by triplets). In this paper, we develop the first method that can deal with the recovery of cameras from noisy fundamental matrices in a general viewing graph. Experimental results demonstrate the promise of the proposed approach on a variety of synthetic and real scenarios.
| null | null | null | null |
[] |
[] |
[] |
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487
|
Not All Frame Features Are Equal: Video-to-4D Generation via Decoupling Dynamic-Static Features
|
[
"Liying Yang",
"Chen Liu",
"Zhenwei Zhu",
"Ajian Liu",
"Hui Ma",
"Jian Nong",
"Yanyan Liang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yang_Not_All_Frame_Features_Are_Equal_Video-to-4D_Generation_via_Decoupling_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yang_Not_All_Frame_Features_Are_Equal_Video-to-4D_Generation_via_Decoupling_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yang_Not_All_Frame_ICCV_2025_supplemental.pdf
|
@InProceedings{Yang_2025_ICCV,
author = {Yang, Liying and Liu, Chen and Zhu, Zhenwei and Liu, Ajian and Ma, Hui and Nong, Jian and Liang, Yanyan},
title = {Not All Frame Features Are Equal: Video-to-4D Generation via Decoupling Dynamic-Static Features},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {7494-7504}
}
|
Recently, the generation of dynamic 3D objects from a video has shown impressive results. Existing methods directly optimize Gaussians using whole information in frames. However, when dynamic regions are interwoven with static regions within frames, particularly if the static regions account for a large proportion, existing methods often overlook information in dynamic regions and are prone to overfitting on static regions. This leads to producing results with blurry textures. We consider that decoupling dynamic-static features to enhance dynamic representations can alleviate this issue. Thus, we propose a dynamic-static feature decoupling module (DSFD). Along temporal axes, it regards the regions of current frame features that possess significant differences relative to reference frame features as dynamic features. Conversely, the remaining parts are the static features. Then, we acquire decoupled features driven by dynamic features and current frame features. Moreover, to further enhance the dynamic representation of decoupled features from different viewpoints and ensure accurate motion prediction, we design a temporal-spatial similarity fusion module (TSSF). Along spatial axes, it adaptively selects similar information of dynamic regions. Hinging on the above, we construct a novel approach, DS4D. Experimental results verify our method achieves state-of-the-art (SOTA) results in video-to-4D. In addition, the experiments on a real-world scenario dataset demonstrate its effectiveness on the 4D scene. Project page: https://github.com/LiyingCV/DS4D.
|
2502.08377
|
Accepted by ICCV2025
| null | null |
[] |
[] |
[] |
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] |
488
|
FedDifRC: Unlocking the Potential of Text-to-Image Diffusion Models in Heterogeneous Federated Learning
|
[
"Huan Wang",
"Haoran Li",
"Huaming Chen",
"Jun Yan",
"Jiahua Shi",
"Jun Shen"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_FedDifRC_Unlocking_the_Potential_of_Text-to-Image_Diffusion_Models_in_Heterogeneous_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_FedDifRC_Unlocking_the_Potential_of_Text-to-Image_Diffusion_Models_in_Heterogeneous_ICCV_2025_paper.pdf
| null |
@InProceedings{Wang_2025_ICCV,
author = {Wang, Huan and Li, Haoran and Chen, Huaming and Yan, Jun and Shi, Jiahua and Shen, Jun},
title = {FedDifRC: Unlocking the Potential of Text-to-Image Diffusion Models in Heterogeneous Federated Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {3726-3736}
}
|
Federated learning aims at training models collaboratively across participants while protecting privacy. However, one major challenge for this paradigm is the data heterogeneity issue, where biased data preferences across multiple clients, harming the model's convergence and performance. In this paper, we first introduce powerful diffusion models into the federated learning paradigm and show that diffusion representations are effective steers during federated training. To explore the possibility of using diffusion representations in handling data heterogeneity, we propose a novel diffusion-inspired Federated paradigm with Diffusion Representation Collaboration, termed FedDifRC, leveraging meaningful guidance of diffusion models to mitigate data heterogeneity. The key idea is to construct text-driven diffusion contrasting and noise-driven diffusion regularization, aiming to provide abundant class-related semantic information and consistent convergence signals. On the one hand, we exploit the conditional feedback from the diffusion model for different text prompts to build a text-driven contrastive learning strategy. On the other hand, we introduce a noise-driven consistency regularization to align local instances with diffusion denoising representations, constraining the optimization region in the feature space. In addition, FedDifRC can be extended to a self-supervised scheme without relying on any labeled data. We also provide a theoretical analysis for FedDifRC to ensure convergence under non-convex objectives. The experiments on different scenarios validate the effectiveness of FedDifRC and the efficiency of crucial components.
|
2507.06482
|
11 Pages, ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
489
|
Vivid4D: Improving 4D Reconstruction from Monocular Video by Video Inpainting
|
[
"Jiaxin Huang",
"Sheng Miao",
"Bangbang Yang",
"Yuewen Ma",
"Yiyi Liao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Huang_Vivid4D_Improving_4D_Reconstruction_from_Monocular_Video_by_Video_Inpainting_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Huang_Vivid4D_Improving_4D_Reconstruction_from_Monocular_Video_by_Video_Inpainting_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Huang_Vivid4D_Improving_4D_ICCV_2025_supplemental.zip
|
@InProceedings{Huang_2025_ICCV,
author = {Huang, Jiaxin and Miao, Sheng and Yang, Bangbang and Ma, Yuewen and Liao, Yiyi},
title = {Vivid4D: Improving 4D Reconstruction from Monocular Video by Video Inpainting},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {12592-12604}
}
|
Reconstructing 4D dynamic scenes from casually captured monocular videos is valuable but highly challenging, as each timestamp is observed from a single viewpoint. We introduce Vivid4D, a novel approach that enhances 4D monocular video synthesis by augmenting observation views -- synthesizing multi-view videos from a monocular input. Unlike existing methods that either solely leverage geometric priors for supervision or use generative priors while overlooking geometry, we integrate both. This reformulates view augmentation as a video inpainting task, where observed views are warped into new viewpoints based on monocular depth priors. To achieve this, we train a video inpainting model on unposed web videos with synthetically generated masks that mimic warping occlusions, ensuring spatially and temporally consistent completion of missing regions. To further mitigate inaccuracies in monocular depth priors, we introduce an iterative view augmentation strategy and a robust reconstruction loss. Experiments demonstrate that our method effectively improves monocular 4D scene reconstruction and completion.
|
2504.11092
| null | null | null |
[] |
[] |
[] |
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] |
490
|
Feather the Throttle: Revisiting Visual Token Pruning for Vision-Language Model Acceleration
|
[
"Mark Endo",
"Xiaohan Wang",
"Serena Yeung-Levy"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Endo_Feather_the_Throttle_Revisiting_Visual_Token_Pruning_for_Vision-Language_Model_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Endo_Feather_the_Throttle_Revisiting_Visual_Token_Pruning_for_Vision-Language_Model_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Endo_Feather_the_Throttle_ICCV_2025_supplemental.pdf
|
@InProceedings{Endo_2025_ICCV,
author = {Endo, Mark and Wang, Xiaohan and Yeung-Levy, Serena},
title = {Feather the Throttle: Revisiting Visual Token Pruning for Vision-Language Model Acceleration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {22826-22835}
}
|
Recent works on accelerating Vision-Language Models achieve strong performance across a variety of vision-language tasks despite highly compressing visual information. In this work, we examine the popular acceleration approach of early pruning of visual tokens inside the language model. Surprisingly, we find that while strong performance is maintained across many tasks, it exhibits drastically different behavior for a subset of vision-centric tasks such as localization. Upon further investigation, we uncover a core issue with the acceleration approach where most tokens towards the top of the image are pruned away. Yet, on many benchmarks aiming to evaluate vision-centric capabilities, strong performance persists with the flawed pruning strategy, highlighting these benchmarks' limited ability to assess fine-grained visual capabilities. Based on these findings, we propose FEATHER (Fast and Effective Acceleration wiTH Ensemble cRiteria), a straightforward approach that resolves the discovered early-layer pruning issue and further enhances the preservation of relevant tokens via multistage pruning with early uniform sampling to ensure broad image coverage. With comparable computational savings, we find that FEATHER achieves more than 5x performance improvement on the vision-centric localization benchmarks compared to the original acceleration approach.
|
2412.13180
|
ICCV 2025, project page:
https://web.stanford.edu/~markendo/projects/feather
| null | null |
[] |
[] |
[] |
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] |
491
|
RePoseD: Efficient Relative Pose Estimation With Known Depth Information
|
[
"Yaqing Ding",
"Viktor Kocur",
"Václav Vávra",
"Zuzana Berger Haladová",
"Jian Yang",
"Torsten Sattler",
"Zuzana Kukelova"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ding_RePoseD_Efficient_Relative_Pose_Estimation_With_Known_Depth_Information_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ding_RePoseD_Efficient_Relative_Pose_Estimation_With_Known_Depth_Information_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ding_RePoseD_Efficient_Relative_ICCV_2025_supplemental.pdf
|
@InProceedings{Ding_2025_ICCV,
author = {Ding, Yaqing and Kocur, Viktor and V\'avra, V\'aclav and Haladov\'a, Zuzana Berger and Yang, Jian and Sattler, Torsten and Kukelova, Zuzana},
title = {RePoseD: Efficient Relative Pose Estimation With Known Depth Information},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {14876-14886}
}
|
Recent advances in monocular depth estimation methods (MDE) and their improved accuracy open new possibilities for their applications. In this paper, we investigate how monocular depth estimates can be used for relative pose estimation. In particular, we are interested in answering the question whether using MDEs improves results over traditional point-based methods. We propose a novel framework for estimating the relative pose of two cameras from point correspondences with associated monocular depths. Since depth predictions are typically defined up to an unknown scale or even both unknown scale and shift parameters, our solvers jointly estimate the scale or both the scale and shift parameters along with the relative pose. We derive efficient solvers considering different types of depths for three camera configurations: (1) two calibrated cameras, (2) two cameras with an unknown shared focal length, and (3) two cameras with unknown different focal lengths. Our new solvers outperform state-of-the-art depth-aware solvers in terms of speed and accuracy. In extensive real experiments on multiple datasets and with various MDEs, we discuss which depth-aware solvers are preferable in which situation. The code is available at https://github.com/kocurvik/mdrp.
|
2501.07742
|
18 pages
| null | null |
[] |
[] |
[] |
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] |
492
|
MEGA: Memory-Efficient 4D Gaussian Splatting for Dynamic Scenes
|
[
"Xinjie Zhang",
"Zhening Liu",
"Yifan Zhang",
"Xingtong Ge",
"Dailan He",
"Tongda Xu",
"Yan Wang",
"Zehong Lin",
"Shuicheng Yan",
"Jun Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_MEGA_Memory-Efficient_4D_Gaussian_Splatting_for_Dynamic_Scenes_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_MEGA_Memory-Efficient_4D_Gaussian_Splatting_for_Dynamic_Scenes_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_MEGA_Memory-Efficient_4D_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Xinjie and Liu, Zhening and Zhang, Yifan and Ge, Xingtong and He, Dailan and Xu, Tongda and Wang, Yan and Lin, Zehong and Yan, Shuicheng and Zhang, Jun},
title = {MEGA: Memory-Efficient 4D Gaussian Splatting for Dynamic Scenes},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27828-27838}
}
|
4D Gaussian Splatting (4DGS) has recently emerged as a promising technique for capturing complex dynamic 3D scenes with high fidelity. It utilizes a 4D Gaussian representation and a GPU-friendly rasterizer, enabling rapid rendering speeds. Despite its advantages, 4DGS faces significant challenges, notably the requirement of millions of 4D Gaussians, each with extensive associated attributes, leading to substantial memory and storage cost. This paper introduces a memory-efficient framework for 4DGS. We streamline the color attribute by decomposing it into a per-Gaussian direct color component with only 3 parameters and a shared lightweight alternating current color predictor. This approach eliminates the need for spherical harmonics coefficients, which typically involve up to 144 parameters in classic 4DGS, thereby creating a memory-efficient 4D Gaussian representation. Furthermore, we introduce an entropy-constrained Gaussian deformation technique that uses a deformation field to expand the action range of each Gaussian and integrates an opacity-based entropy loss to limit the number of Gaussians, thus forcing our model to use as few Gaussians as possible to fit a dynamic scene well. With simple half-precision storage and zip compression, our framework achieves a storage reduction by approximately 190xand 125xon the Technicolor and Neural 3D Video datasets, respectively, compared to the original 4DGS. Meanwhile, it maintains comparable rendering speeds and scene representation quality, setting a new standard in the field. Code is available at https://github.com/Xinjie-Q/MEGA.
|
2410.13613
|
Accepted by ICCV 2025
|
https://github.com/Xinjie-Q/MEGA
| null |
[] |
[] |
[] |
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] |
493
|
Beyond Label Semantics: Language-Guided Action Anatomy for Few-shot Action Recognition
|
[
"Zefeng Qian",
"Xincheng Yao",
"Yifei Huang",
"Chongyang Zhang",
"Jiangyong Ying",
"Hong Sun"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Qian_Beyond_Label_Semantics_Language-Guided_Action_Anatomy_for_Few-shot_Action_Recognition_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Qian_Beyond_Label_Semantics_Language-Guided_Action_Anatomy_for_Few-shot_Action_Recognition_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Qian_Beyond_Label_Semantics_ICCV_2025_supplemental.pdf
|
@InProceedings{Qian_2025_ICCV,
author = {Qian, Zefeng and Yao, Xincheng and Huang, Yifei and Zhang, Chongyang and Ying, Jiangyong and Sun, Hong},
title = {Beyond Label Semantics: Language-Guided Action Anatomy for Few-shot Action Recognition},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10421-10431}
}
|
Few-shot action recognition (FSAR) aims to classify human actions in videos with only a small number of labeled samples per category. The scarcity of training data has driven recent efforts to incorporate additional modalities, particularly text. However, the subtle variations in human posture, motion dynamics, and the object interactions that occur during different phases, are critical inherent knowledge of actions that cannot be fully exploited by action labels alone. In this work, we propose Language-Guided Action Anatomy (LGA), a novel framework that goes beyond label semantics by leveraging Large Language Models (LLMs) to dissect the essential representational characteristics hidden beneath action labels. Guided by the prior knowledge encoded in LLM, LGA effectively captures rich spatiotemporal cues in few-shot scenarios. Specifically, for text, we prompt an off-the-shelf LLM to anatomize labels into sequences of atomic action descriptions, focusing on the three core elements of action (subject, motion, object). For videos, a Visual Anatomy Module segments actions into atomic video phases to capture the sequential structure of actions. A fine-grained fusion strategy then integrates textual and visual features at the atomic level, resulting in more generalizable prototypes. Finally, we introduce a Multimodal Matching mechanism, comprising both video-video and video-text matching, to ensure robust few-shot classification. Experimental results demonstrate that LGA achieves state-of-the-art performance across multiple FSAR benchmarks.
|
2507.16287
|
Accepted by ICCV2025
| null | null |
[] |
[] |
[] |
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] |
494
|
VoluMe - Authentic 3D Video Calls from Live Gaussian Splat Prediction
|
[
"Martin de La Gorce",
"Charlie Hewitt",
"Tibor Takács",
"Robert Gerdisch",
"Zafiirah Hosenie",
"Givi Meishvili",
"Marek Kowalski",
"Thomas J. Cashman",
"Antonio Criminisi"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/de_La_Gorce_VoluMe_-_Authentic_3D_Video_Calls_from_Live_Gaussian_Splat_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/de_La_Gorce_VoluMe_-_Authentic_3D_Video_Calls_from_Live_Gaussian_Splat_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/de_La_Gorce_VoluMe_-_Authentic_ICCV_2025_supplemental.zip
|
@InProceedings{de_La_Gorce_2025_ICCV,
author = {de La Gorce, Martin and Hewitt, Charlie and Tak\'acs, Tibor and Gerdisch, Robert and Hosenie, Zafiirah and Meishvili, Givi and Kowalski, Marek and Cashman, Thomas J. and Criminisi, Antonio},
title = {VoluMe - Authentic 3D Video Calls from Live Gaussian Splat Prediction},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13783-13792}
}
|
Virtual 3D meetings offer the potential to enhance copresence, increase engagement and thus improve effectiveness of remote meetings compared to standard 2D video calls. However, representing people in 3D meetings remains a challenge; existing solutions achieve high quality by using complex hardware, making use of fixed appearance via enrolment, or by inverting a pre-trained generative model. These approaches lead to constraints that are unwelcome and ill-fitting for videoconferencing applications.We present the first method to predict 3D Gaussian reconstructions in real time from a single 2D webcam feed, where the 3D representation is not only live and realistic, but also authentic to the input video. By conditioning the 3D representation on each video frame independently, our reconstruction faithfully recreates the input video from the captured viewpoint (a property we call authenticity), while generalizing realistically to novel viewpoints. Additionally, we introduce a stability loss to obtain reconstructions that are temporally stable on video sequences.We show that our method delivers state-of-the-art accuracy in visual quality and stability metrics compared to existing methods, and demonstrate our approach in live one-to-one 3D meetings using only a standard 2D camera and display. This demonstrates that our approach can allow anyone to communicate volumetrically, via a method for 3D videoconferencing that is not only highly accessible, but also realistic and authentic.
|
2507.21311
| null | null | null |
[] |
[] |
[] |
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] |
495
|
Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation
|
[
"Bozhong Zheng",
"Jinye Gan",
"Xiaohao Xu",
"Xintao Chen",
"Wenqiao Li",
"Xiaonan Huang",
"Na Ni",
"Yingna Wu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zheng_Bridging_3D_Anomaly_Localization_and_Repair_via_High-Quality_Continuous_Geometric_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zheng_Bridging_3D_Anomaly_Localization_and_Repair_via_High-Quality_Continuous_Geometric_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zheng_Bridging_3D_Anomaly_ICCV_2025_supplemental.pdf
|
@InProceedings{Zheng_2025_ICCV,
author = {Zheng, Bozhong and Gan, Jinye and Xu, Xiaohao and Chen, Xintao and Li, Wenqiao and Huang, Xiaonan and Ni, Na and Wu, Yingna},
title = {Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27063-27072}
}
|
3D point cloud anomaly detection is essential for robust vision systems but is challenged by pose variations and complex geometric anomalies. Existing patch-based methods often suffer from geometric fidelity issues due to discrete voxelization or projection-based representations, limiting fine-grained anomaly localization.We introduce Pose-Aware Signed Distance Field (PASDF), a novel framework that integrates 3D anomaly detection and repair by learning a continuous, pose-invariant shape representation. PASDF leverages a Pose Alignment Module for canonicalization and a SDF Network to dynamically incorporate pose, enabling implicit learning of high-fidelity anomaly repair templates from the continuous SDF. This facilitates precise pixel-level anomaly localization through an Anomaly-Aware Scoring Module.Crucially, the continuous 3D representation in PASDF extends beyond detection, facilitating in-situ anomaly repair. Experiments on Real3D-AD and Anomaly-ShapeNet demonstrate state-of-the-art performance, achieving high object-level AUROC scores of 80.2% and 90.0%, respectively. These results highlight the effectiveness of continuous geometric representations in advancing 3D anomaly detection and facilitating practical anomaly region repair. Our code will be released to drive further research.
|
2505.24431
| null |
https://github.com/ZZZBBBZZZ/PASDF
| null |
[] |
[] |
[] |
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] |
496
|
MonoMobility: Zero-Shot 3D Mobility Analysis from Monocular Videos
|
[
"Hongyi Zhou",
"Yulan Guo",
"Xiaogang Wang",
"Kai Xu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhou_MonoMobility_Zero-Shot_3D_Mobility_Analysis_from_Monocular_Videos_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_MonoMobility_Zero-Shot_3D_Mobility_Analysis_from_Monocular_Videos_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhou_MonoMobility_Zero-Shot_3D_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhou_2025_ICCV,
author = {Zhou, Hongyi and Guo, Yulan and Wang, Xiaogang and Xu, Kai},
title = {MonoMobility: Zero-Shot 3D Mobility Analysis from Monocular Videos},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {8800-8809}
}
|
Accurately analyzing the motion parts and their motion attributes in dynamic environments is crucial for advancing key areas such as embodied intelligence. Addressing the limitations of existing methods that rely on dense multi-view images or detailed part-level annotations, we propose an innovative framework that can analyze 3D mobility from monocular videos in a zero-shot manner. This framework can precisely parse motion parts and motion attributes only using a monocular video, completely eliminating the need for annotated training data. Specifically, our method first constructs the scene geometry and roughly analyzes the motion parts and their initial motion attributes combining depth estimation, optical flow analysis and point cloud registration method, then employs 2D Gaussian splatting for scene representation. Building on this, we introduce an end-to-end dynamic scene optimization algorithm specifically designed for articulated objects, refining the initial analysis results to ensure the system can handle 'rotation', 'translation', and even complex movements ('rotation+translation'), demonstrating high flexibility and versatility. To validate the robustness and wide applicability of our method, we created a comprehensive dataset comprising both simulated and real-world scenarios. Experimental results show that our framework can effectively analyze articulated object motions in an annotation-free manner, showcasing its significant potential in future embodied intelligence applications.
|
2505.11868
| null | null | null |
[] |
[] |
[] |
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] |
497
|
Prior-aware Dynamic Temporal Modeling Framework for Sequential 3D Hand Pose Estimation
|
[
"Pengfei Ren",
"Jingyu Wang",
"Haifeng Sun",
"Qi Qi",
"Xingyu Liu",
"Menghao Zhang",
"Lei Zhang",
"Jing Wang",
"Jianxin Liao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ren_Prior-aware_Dynamic_Temporal_Modeling_Framework_for_Sequential_3D_Hand_Pose_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ren_Prior-aware_Dynamic_Temporal_Modeling_Framework_for_Sequential_3D_Hand_Pose_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ren_Prior-aware_Dynamic_Temporal_ICCV_2025_supplemental.pdf
|
@InProceedings{Ren_2025_ICCV,
author = {Ren, Pengfei and Wang, Jingyu and Sun, Haifeng and Qi, Qi and Liu, Xingyu and Zhang, Menghao and Zhang, Lei and Wang, Jing and Liao, Jianxin},
title = {Prior-aware Dynamic Temporal Modeling Framework for Sequential 3D Hand Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {6476-6487}
}
|
3D hand pose estimation plays a critical role in various human-computer interaction tasks. Single-frame 3D hand pose estimation methods have poor temporal smoothness and are easily affected by self-occlusion, which severely impacts their practical applicability. Traditional joint-based sequential pose estimation methods primarily focus on the human body and struggle to handle the complex hand structure, high degrees of freedom in hand motion, and rapidly changing hand motion trends. To address these challenges, we propose a prior-aware dynamic temporal modeling framework for sequential 3D hand pose estimation. We introduce a flexible memory mechanism to model hand prior information, which alleviates the scale and depth ambiguity in single-frame hand pose estimation. Additionally, we propose a dynamic temporal convolution module that adjusts the receptive field size and feature aggregation weights based on the motion information at each moment, effectively capturing rapid motion trends. By decoupling dynamic temporal modeling at the joint and hand levels, our method captures both subtle short-term variations and long-term motion trends, significantly improving the smoothness and accuracy of hand pose estimation. Experiments on four public datasets demonstrate that our method achieves the state-of-the-art results in terms of hand pose estimation accuracy and temporal smoothness.
| null | null | null | null |
[] |
[] |
[] |
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] |
498
|
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery
|
[
"Xiao Liu",
"Nan Pu",
"Haiyang Zheng",
"Wenjing Li",
"Nicu Sebe",
"Zhun Zhong"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_Generate_Refine_and_Encode_Leveraging_Synthesized_Novel_Samples_for_On-the-Fly_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_Generate_Refine_and_Encode_Leveraging_Synthesized_Novel_Samples_for_On-the-Fly_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_Generate_Refine_and_ICCV_2025_supplemental.pdf
|
@InProceedings{Liu_2025_ICCV,
author = {Liu, Xiao and Pu, Nan and Zheng, Haiyang and Li, Wenjing and Sebe, Nicu and Zhong, Zhun},
title = {Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1078-1087}
}
|
In this paper, we investigate a practical yet challenging task: On-the-fly Category Discovery (OCD). This task focuses on the online identification of newly arriving stream data that may belong to both known and unknown categories, utilizing the category knowledge from only labeled data. Existing OCD methods are devoted to fully mining transferable knowledge from only labeled data. However, the transferability learned by these methods is limited because the knowledge contained in known categories is often insufficient, especially when few annotated data/categories are available in fine-grained recognition. To mitigate this limitation, we propose a diffusion-based OCD framework, dubbed DiffGRE, which integrates Generation, Refinement, and Encoding in a multi-stage fashion. Specifically, we first design an attribute-composition generation method based on cross-image interpolation in the diffusion latent space to synthesize novel samples. Then, we propose a diversity-driven refinement approach to select the synthesized images that differ from known categories for subsequent OCD model training. Finally, we leverage a semi-supervised leader encoding to inject additional category knowledge contained in synthesized data into the OCD models, which can benefit the discovery of both known and unknown categories during the on-the-fly inference process. Extensive experiments demonstrate the superiority of our DiffGRE over previous methods on six fine-grained datasets.
|
2507.04051
|
ICCV 2025
| null | null |
[] |
[] |
[] |
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499
|
Visual-RFT: Visual Reinforcement Fine-Tuning
|
[
"Ziyu Liu",
"Zeyi Sun",
"Yuhang Zang",
"Xiaoyi Dong",
"Yuhang Cao",
"Haodong Duan",
"Dahua Lin",
"Jiaqi Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_Visual-RFT_Visual_Reinforcement_Fine-Tuning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_Visual-RFT_Visual_Reinforcement_Fine-Tuning_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_Visual-RFT_Visual_Reinforcement_ICCV_2025_supplemental.pdf
|
@InProceedings{Liu_2025_ICCV,
author = {Liu, Ziyu and Sun, Zeyi and Zang, Yuhang and Dong, Xiaoyi and Cao, Yuhang and Duan, Haodong and Lin, Dahua and Wang, Jiaqi},
title = {Visual-RFT: Visual Reinforcement Fine-Tuning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2034-2044}
}
|
Reinforcement Fine-Tuning (RFT) in Large Reasoning Models like OpenAI o1 learns from feedback on its answers, which is especially useful in applications when fine-tuning data is scarce.Recent open-source work like DeepSeek-R1 demonstrates that reinforcement learning with verifiable reward is possibly one key direction in reproducing o1.While the R1-style model has demonstrated success in language models, its application in multi-modal domains remains under-explored.This work introduces Visual Reinforcement Fine-Tuning (Visual-RFT), which further extends the application areas of RFT on visual tasks.Specifically, Visual-RFT first uses Large Vision-Language Models (LVLMs) to generate multiple responses containing reasoning tokens and final answers for each input, and then uses our proposed visual perception verifiable reward functions to update the model via the policy optimization algorithm such as Group Relative Policy Optimization (GRPO).We design different verifiable reward functions for different perception tasks, such as the Intersection over Union (IoU) reward for object detection.Experimental results on fine-grained image classification, few-shot object detection, reasoning grounding, as well as open-vocabulary object detection benchmarks show the competitive performance and advanced generalization ability of Visual-RFT compared with Supervised Fine-tuning (SFT).For example, Visual-RFT improves accuracy by 24.3% over the baseline in one-shot fine-grained image classification with around 100 samples.In few-shot object detection, Visual-RFT also exceeds the baseline by 21.0 on COCO's 4-shot setting and 15.4 on LVIS.Our Visual-RFT represents a paradigm shift in fine-tuning LVLMs, offering a data-efficient, reward-driven approach that enhances reasoning and adaptability for domain-specific tasks.
| null | null | null | null |
[] |
[] |
[] |
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