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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
300
|
LoRAverse: A Submodular Framework to Retrieve Diverse Adapters for Diffusion Models
|
[
"Mert Sonmezer",
"Matthew Zheng",
"Pinar Yanardag"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Sonmezer_LoRAverse_A_Submodular_Framework_to_Retrieve_Diverse_Adapters_for_Diffusion_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Sonmezer_LoRAverse_A_Submodular_Framework_to_Retrieve_Diverse_Adapters_for_Diffusion_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Sonmezer_LoRAverse_A_Submodular_ICCV_2025_supplemental.pdf
|
@InProceedings{Sonmezer_2025_ICCV,
author = {Sonmezer, Mert and Zheng, Matthew and Yanardag, Pinar},
title = {LoRAverse: A Submodular Framework to Retrieve Diverse Adapters for Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {17879-17888}
}
|
Low-rank Adaptation (LoRA) models have revolutionized the personalization of pre-trained diffusion models by enabling fine-tuning through low-rank, factorized weight matrices specifically optimized for attention layers. These models facilitate the generation of highly customized content across a variety of objects, individuals, and artistic styles without the need for extensive retraining. Despite the availability of over 100K LoRA adapters on platforms like Civit.ai, users often face challenges in navigating, selecting, and effectively utilizing the most suitable adapters due to their sheer volume, diversity, and lack of structured organization. This paper addresses the problem of selecting the most relevant and diverse LoRA models from this vast database by framing the task as a combinatorial optimization problem and proposing a novel submodular framework. Our quantitative and qualitative experiments demonstrate that our method generates diverse outputs across a wide range of domains.
| null | null | null | null |
[] |
[] |
[] |
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] |
301
|
AllTracker: Efficient Dense Point Tracking at High Resolution
|
[
"Adam W. Harley",
"Yang You",
"Xinglong Sun",
"Yang Zheng",
"Nikhil Raghuraman",
"Yunqi Gu",
"Sheldon Liang",
"Wen-Hsuan Chu",
"Achal Dave",
"Suya You",
"Rares Ambrus",
"Katerina Fragkiadaki",
"Leonidas Guibas"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Harley_AllTracker_Efficient_Dense_Point_Tracking_at_High_Resolution_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Harley_AllTracker_Efficient_Dense_Point_Tracking_at_High_Resolution_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Harley_AllTracker_Efficient_Dense_ICCV_2025_supplemental.pdf
|
@InProceedings{Harley_2025_ICCV,
author = {Harley, Adam W. and You, Yang and Sun, Xinglong and Zheng, Yang and Raghuraman, Nikhil and Gu, Yunqi and Liang, Sheldon and Chu, Wen-Hsuan and Dave, Achal and You, Suya and Ambrus, Rares and Fragkiadaki, Katerina and Guibas, Leonidas},
title = {AllTracker: Efficient Dense Point Tracking at High Resolution},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {5253-5262}
}
|
We introduce AllTracker: a model that estimates long-range point tracks by way of estimating the flow field between a query frame and every other frame of a video. Unlike existing point tracking methods, our approach delivers high-resolution and dense (all-pixel) correspondence fields, which can be visualized as flow maps. Unlike existing optical flow methods, our approach corresponds one frame to hundreds of subsequent frames, rather than just the next frame. We develop a new architecture for this task, blending techniques from existing work in optical flow and point tracking: the model performs iterative inference on low-resolution grids of correspondence estimates, propagating information spatially via 2D convolution layers, and propagating information temporally via pixel-aligned attention layers. The model is fast and parameter-efficient (16 million parameters), and delivers state-of-the-art point tracking accuracy at high resolution (i.e., tracking 768x1024 pixels, on a 40G GPU). A benefit of our design is that we can train jointly on optical flow datasets and point tracking datasets, and we find that doing so is crucial for top performance. We provide an extensive ablation study on our architecture details and training recipe, making it clear which details matter most. Our code and model weights are available: https://alltracker.github.io
|
2506.07310
| null | null | null |
[
"aharley/alltracker"
] |
[
"aharley/alltracker"
] |
[] |
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] |
302
|
Gaussian-based World Model: Gaussian Priors for Voxel-Based Occupancy Prediction and Future Motion Prediction
|
[
"Tuo Feng",
"Wenguan Wang",
"Yi Yang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Feng_Gaussian-based_World_Model_Gaussian_Priors_for_Voxel-Based_Occupancy_Prediction_and_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Feng_Gaussian-based_World_Model_Gaussian_Priors_for_Voxel-Based_Occupancy_Prediction_and_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Feng_Gaussian-based_World_Model_ICCV_2025_supplemental.pdf
|
@InProceedings{Feng_2025_ICCV,
author = {Feng, Tuo and Wang, Wenguan and Yang, Yi},
title = {Gaussian-based World Model: Gaussian Priors for Voxel-Based Occupancy Prediction and Future Motion Prediction},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {25239-25249}
}
|
In autonomous driving, accurately predicting occupancy and motion is crucial for safe navigation within dynamic environments. However, existing methods often suffer from difficulties in handling complex scenes and uncertainty arising from sensor data. To address these issues, we propose a new Gaussian-based World Model (GWM), seamlessly integrating raw multi-modal sensor inputs. In 1st stage, Gaussian representation learner utilizes self-supervised pretraining to learn robust Gaussian representation. Gaussian representation integrates semantic and geometric information and establishes a robust probabilistic understanding of the environment. In 2nd stage, GWM seamlessly integrates learning, simulation, and planning into a unified framework, empowering the uncertainty-aware simulator & planner to jointly forecast future scene evolutions and vehicle trajectories. Simulator generates future scene predictions by modeling both static and dynamic elements, while planner calculates optimal paths to minimize collision risks, thus enhancing navigation safety. Overall, GWM employs a sensor-to-planning world model that directly processes raw sensor data, setting it apart from previous methods. Experiments show that GWM outperforms state-of-the-art approaches by 1.46% in semantic comprehension and 0.07m in motion prediction. Moreover, we provide an in-depth analysis of Gaussian representations under complex scenarios.
| null | null | null | null |
[] |
[] |
[] |
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] |
303
|
CombatVLA: An Efficient Vision-Language-Action Model for Combat Tasks in 3D Action Role-Playing Games
|
[
"Peng Chen",
"Pi Bu",
"Yingyao Wang",
"Xinyi Wang",
"Ziming Wang",
"Jie Guo",
"Yingxiu Zhao",
"Qi Zhu",
"Jun Song",
"Siran Yang",
"Jiamang Wang",
"Bo Zheng"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_CombatVLA_An_Efficient_Vision-Language-Action_Model_for_Combat_Tasks_in_3D_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_CombatVLA_An_Efficient_Vision-Language-Action_Model_for_Combat_Tasks_in_3D_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_CombatVLA_An_Efficient_ICCV_2025_supplemental.pdf
|
@InProceedings{Chen_2025_ICCV,
author = {Chen, Peng and Bu, Pi and Wang, Yingyao and Wang, Xinyi and Wang, Ziming and Guo, Jie and Zhao, Yingxiu and Zhu, Qi and Song, Jun and Yang, Siran and Wang, Jiamang and Zheng, Bo},
title = {CombatVLA: An Efficient Vision-Language-Action Model for Combat Tasks in 3D Action Role-Playing Games},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10919-10928}
}
|
Recent advances in Vision-Language-Action models (VLAs) have expanded the capabilities of embodied intelligence. However, significant challenges remain in real-time decision-making in complex 3D environments, which demand second-level responses, high-resolution perception, and tactical reasoning under dynamic conditions. To advance the field, we introduce CombatVLA, an efficient VLA model optimized for combat tasks in 3D action role-playing games(ARPGs). Specifically, our CombatVLA is a 3B model trained on video-action pairs collected by an action tracker, where the data is formatted as action-of-thought (AoT) sequences. Thereafter, CombatVLA seamlessly integrates into an action execution framework, allowing efficient inference through our truncated AoT strategy. Experimental results demonstrate that CombatVLA not only outperforms all existing models on the combat understanding benchmark but also achieves a 50-fold acceleration in game combat. Moreover, it has a higher task success rate than human players. We will release all resources, including the action tracker, dataset, model weights, training code, and action execution framework implementation at https://combatvla.github.io/.
|
2503.09527
| null | null | null |
[] |
[] |
[] |
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] |
304
|
SketchSplat: 3D Edge Reconstruction via Differentiable Multi-view Sketch Splatting
|
[
"Haiyang Ying",
"Matthias Zwicker"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ying_SketchSplat_3D_Edge_Reconstruction_via_Differentiable_Multi-view_Sketch_Splatting_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ying_SketchSplat_3D_Edge_Reconstruction_via_Differentiable_Multi-view_Sketch_Splatting_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ying_SketchSplat_3D_Edge_ICCV_2025_supplemental.zip
|
@InProceedings{Ying_2025_ICCV,
author = {Ying, Haiyang and Zwicker, Matthias},
title = {SketchSplat: 3D Edge Reconstruction via Differentiable Multi-view Sketch Splatting},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {25649-25659}
}
|
Edges are one of the most basic parametric primitives to describe structural information in 3D. In this paper, we study parametric 3D edge reconstruction from calibrated multi-view images. Previous methods usually reconstruct a 3D edge point set from multi-view 2D edge images, and then fit 3D edges to the point set. However, noise in the point set may cause gaps among fitted edges, and the recovered edges may not align with input multi-view images since the edge fitting depends only on the reconstructed 3D point set. To mitigate these problems, we propose SketchSplat, a method to reconstruct accurate, complete, and compact 3D edges via differentiable multi-view sketch splatting. We represent 3D edges as sketches, which are parametric lines and curves defined by attributes including control points, scales, and opacity. During reconstruction, we iteratively sample Gaussian points from a set of sketches and rasterize the Gaussians onto 2D edge images. Then the gradient of the image loss can be back-propagated to optimize the sketch attributes. Our method bridges 2D edge images and 3D edges in a differentiable manner, which ensures that 3D edges align well with 2D images and leads to accurate and complete results. We also propose a series of adaptive topological operations to reduce redundant edges and apply them along with the sketch optimization, yielding a more compact reconstruction. Finally, we contribute an accurate 2D edge detector that improves the performance of both ours and existing methods. Experiments show that our method achieves state-of-the-art accuracy, completeness, and compactness on a benchmark CAD dataset.
|
2503.14786
| null | null |
https://oceanying.github.io/SketchSplat/
|
[] |
[] |
[] |
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] |
305
|
Social Debiasing for Fair Multi-modal LLMs
|
[
"Harry Cheng",
"Yangyang Guo",
"Qingpei Guo",
"Ming Yang",
"Tian Gan",
"Weili Guan",
"Liqiang Nie"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Cheng_Social_Debiasing_for_Fair_Multi-modal_LLMs_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Cheng_Social_Debiasing_for_Fair_Multi-modal_LLMs_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Cheng_Social_Debiasing_for_ICCV_2025_supplemental.pdf
|
@InProceedings{Cheng_2025_ICCV,
author = {Cheng, Harry and Guo, Yangyang and Guo, Qingpei and Yang, Ming and Gan, Tian and Guan, Weili and Nie, Liqiang},
title = {Social Debiasing for Fair Multi-modal LLMs},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1740-1750}
}
|
Multi-modal Large Language Models (MLLMs) have dramatically advanced the research field and delivered powerful vision-language understanding capabilities. However, these models often inherit deep-rooted social biases from their training data, leading to uncomfortable responses with respect to attributes such as race and gender. This paper addresses the issue of social biases in MLLMs by i) introducing a comprehensive counterfactual dataset with multiple social concepts (CMSC), which complements existing datasets by providing 18 diverse and balanced social concepts; and ii) proposing a counter-stereotype debiasing (CSD) strategy that mitigates social biases in MLLMs by leveraging the opposites of prevalent stereotypes. CSD incorporates both a novel bias-aware data sampling method and a loss rescaling method, enabling the model to effectively reduce biases. We conduct extensive experiments with four prevalent MLLM architectures. The results demonstrate the advantage of the CMSC dataset and the edge of CSD strategy in reducing social biases compared to existing competing methods, without compromising the overall performance on general multi-modal reasoning benchmarks.
|
2408.06569
|
Project page:
https://github.com/xaCheng1996/Social_Debiasing_For_Fair_MLLMs
| null | null |
[] |
[] |
[] |
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] |
306
|
DOGR: Towards Versatile Visual Document Grounding and Referring
|
[
"Yinan Zhou",
"Yuxin Chen",
"Haokun Lin",
"Yichen Wu",
"Shuyu Yang",
"Zhongang Qi",
"Chen Ma",
"Li Zhu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhou_DOGR_Towards_Versatile_Visual_Document_Grounding_and_Referring_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_DOGR_Towards_Versatile_Visual_Document_Grounding_and_Referring_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhou_DOGR_Towards_Versatile_ICCV_2025_supplemental.zip
|
@InProceedings{Zhou_2025_ICCV,
author = {Zhou, Yinan and Chen, Yuxin and Lin, Haokun and Wu, Yichen and Yang, Shuyu and Qi, Zhongang and Ma, Chen and Zhu, Li},
title = {DOGR: Towards Versatile Visual Document Grounding and Referring},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {3596-3606}
}
|
With recent advances in Multimodal Large Language Models (MLLMs), grounding and referring capabilities have gained increasing attention for achieving detailed understanding and flexible user interaction. However, these capabilities still remain underdeveloped in visual document understanding due to the scarcity of fine-grained datasets and comprehensive benchmarks. To fill this gap, we propose the **DO**cument **G**rounding and **R**eferring data engine (**DOGR-Engine**), which generates two types of high-quality fine-grained document data: (1) multi-granular parsing data to improve text localization and recognition, and (2) instruction-tuning data to activate MLLMs' grounding and referring capabilities in dialogue and reasoning. Using the DOGR-Engine, we construct **DOGR-Bench**, a benchmark covering seven grounding and referring tasks across three document types (chart, poster, and PDF document), offering a comprehensive evaluation of fine-grained document understanding. Leveraging the generated data, we further develop **DOGR**, a strong baseline model that excels in text localization and recognition, while precisely grounds and refers to key textual information during conversation and reasoning, thereby advancing document understanding to a finer granularity and enable flexible interaction paradigms. Our code, data, and model are open-sourced at https://github.com/zyinan99/DOGR.
|
2411.17125
|
22 pages, 16 figures
| null | null |
[] |
[] |
[] |
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307
|
Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation
|
[
"Yujie Zhang",
"Bingyang Cui",
"Qi Yang",
"Zhu Li",
"Yiling Xu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_Benchmarking_and_Learning_Multi-Dimensional_Quality_Evaluator_for_Text-to-3D_Generation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_Benchmarking_and_Learning_Multi-Dimensional_Quality_Evaluator_for_Text-to-3D_Generation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_Benchmarking_and_Learning_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Yujie and Cui, Bingyang and Yang, Qi and Li, Zhu and Xu, Yiling},
title = {Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {18563-18574}
}
|
Text-to-3D generation has achieved remarkable progress in recent years, yet evaluating these methods remains challenging for two reasons: i) existing benchmarks lack fine-grained evaluation on different prompt categories and evaluation dimensions; ii) previous evaluation metrics only focus on a single aspect (e.g., text-3D alignment) and fail to perform multi-dimensional quality assessment. To address these problems, we first propose a comprehensive benchmark named MATE-3D. The benchmark contains eight well-designed prompt categories that cover single and multiple object generation, resulting in 1,280 generated textured meshes. We have conducted a large-scale subjective experiment from four different evaluation dimensions and collected 107,520 annotations, followed by detailed analyses of the results. Based on MATE-3D, we propose a novel quality evaluator named HyperScore. Utilizing hypernetwork to generate specified mapping functions for each evaluation dimension, our metric can effectively perform multi-dimensional quality assessment. HyperScore presents superior performance over existing metrics on MATE-3D, making it a promising metric for assessing and improving text-to-3D generation.
|
2412.11170
| null | null | null |
[] |
[] |
[
"ccccby/MATE-3D"
] |
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] |
308
|
Sat2City: 3D City Generation from A Single Satellite Image with Cascaded Latent Diffusion
|
[
"Tongyan Hua",
"Lutao Jiang",
"Ying-Cong Chen",
"Wufan Zhao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Hua_Sat2City_3D_City_Generation_from_A_Single_Satellite_Image_with_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Hua_Sat2City_3D_City_Generation_from_A_Single_Satellite_Image_with_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Hua_Sat2City_3D_City_ICCV_2025_supplemental.pdf
|
@InProceedings{Hua_2025_ICCV,
author = {Hua, Tongyan and Jiang, Lutao and Chen, Ying-Cong and Zhao, Wufan},
title = {Sat2City: 3D City Generation from A Single Satellite Image with Cascaded Latent Diffusion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27978-27988}
}
|
Recent advancements in generative models have enabled 3D urban scene generation from satellite imagery, unlocking promising applications in gaming, digital twins, and beyond. However, most existing methods rely heavily on neural rendering techniques, which hinder their ability to produce detailed 3D structures on a broader scale, largely due to the inherent structural ambiguity derived from relatively limited 2D observations. To address this challenge, we propose Sat2City, a novel framework that synergizes the representational capacity of sparse voxel grids with latent diffusion models, tailored specifically for our novel 3D city dataset. Our approach is enabled by three key components: (1) A cascaded latent diffusion framework that progressively recovers 3D city structures from satellite imagery, (2) a Re-Hash operation at its Variational Autoencoder (VAE) bottleneck to compute multi-scale feature grids for stable appearance optimization, and (3) an inverse sampling strategy enabling implicit supervision for smooth appearance transitioning. To overcome the challenge of collecting real-world city-scale 3D models with high-quality geometry and appearance, we introduce a dataset of synthesized large-scale 3D cities paired with satellite-view height maps. Validated on this dataset, our framework generates detailed 3D structures from a single satellite image, achieving superior fidelity compared to existing city generation models.
|
2507.04403
|
ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
309
|
Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridging
|
[
"Chongjie Ye",
"Yushuang Wu",
"Ziteng Lu",
"Jiahao Chang",
"Xiaoyang Guo",
"Jiaqing Zhou",
"Hao Zhao",
"Xiaoguang Han"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ye_Hi3DGen_High-fidelity_3D_Geometry_Generation_from_Images_via_Normal_Bridging_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ye_Hi3DGen_High-fidelity_3D_Geometry_Generation_from_Images_via_Normal_Bridging_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ye_Hi3DGen_High-fidelity_3D_ICCV_2025_supplemental.zip
|
@InProceedings{Ye_2025_ICCV,
author = {Ye, Chongjie and Wu, Yushuang and Lu, Ziteng and Chang, Jiahao and Guo, Xiaoyang and Zhou, Jiaqing and Zhao, Hao and Han, Xiaoguang},
title = {Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridging},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {25050-25061}
}
|
With the growing demand for high-fidelity 3D models from 2D images, existing methods still face significant challenges in accurately reproducing fine-grained geometric details due to limitations in domain gaps and inherent ambiguities in RGB images. To address these issues, we propose Hi3DGen, a novel framework for generating highfidelity 3D geometry from images via normal bridging. Hi3DGen consists of three key components: 1. an imageto-normal estimator that decouples the low-high frequency image pattern with noise injection and dual-stream training to achieve generalizable, stable, and sharp estimation; 2. a normal-to-geometry learning approach that uses normalregularized latent diffusion learning to enhance 3D geometry generation fidelity; and 3. a 3D data synthesis pipeline that constructs a high-quality dataset to support training. Extensive experiments demonstrate the effectiveness and superiority of our framework in generating rich geometric details, outperforming state-of-the-art methods in terms of fidelity. Our work provides a new direction for high-fidelity 3D geometry generation from images by leveraging normal maps as an intermediate representation.
|
2503.22236
|
https://stable-x.github.io/Hi3DGen
| null | null |
[] |
[] |
[] |
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310
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SC-Captioner: Improving Image Captioning with Self-Correction by Reinforcement Learning
|
[
"Lin Zhang",
"Xianfang Zeng",
"Kangcong Li",
"Gang Yu",
"Tao Chen"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_SC-Captioner_Improving_Image_Captioning_with_Self-Correction_by_Reinforcement_Learning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_SC-Captioner_Improving_Image_Captioning_with_Self-Correction_by_Reinforcement_Learning_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_SC-Captioner_Improving_Image_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Lin and Zeng, Xianfang and Li, Kangcong and Yu, Gang and Chen, Tao},
title = {SC-Captioner: Improving Image Captioning with Self-Correction by Reinforcement Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {23145-23155}
}
|
We propose SC-Captioner, a reinforcement learning framework that enables the self-correcting capability of image caption models. Our crucial technique lies in the design of the reward function to incentivize accurate caption corrections. Specifically, the predicted and reference captions are decomposed into object, attribute, and relation sets using scene-graph parsing algorithms. We calculate the set difference between sets of initial and self-corrected captions to identify added and removed elements. These elements are matched against the reference sets to calculate correctness bonuses for accurate refinements and mistake punishments for wrong additions and removals, thereby forming the final reward. For image caption quality assessment, we propose a set of metrics refined from CAPTURE that alleviate its incomplete precision evaluation and inefficient relation matching problems. Furthermore, we collect a fine-grained annotated image caption dataset, RefinedCaps, consisting of 6.5K diverse images from COCO dataset. Experiments show that applying SC-Captioner on large visual-language models can generate better image captions across various scenarios, significantly outperforming the direct preference optimization training strategy.
| null | null | null | null |
[] |
[] |
[] |
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] |
311
|
DreamDance: Animating Human Images by Enriching 3D Geometry Cues from 2D Poses
|
[
"Yatian Pang",
"Bin Zhu",
"Bin Lin",
"Mingzhe Zheng",
"Francis E. H. Tay",
"Ser-Nam Lim",
"Harry Yang",
"Li Yuan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Pang_DreamDance_Animating_Human_Images_by_Enriching_3D_Geometry_Cues_from_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Pang_DreamDance_Animating_Human_Images_by_Enriching_3D_Geometry_Cues_from_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Pang_DreamDance_Animating_Human_ICCV_2025_supplemental.pdf
|
@InProceedings{Pang_2025_ICCV,
author = {Pang, Yatian and Zhu, Bin and Lin, Bin and Zheng, Mingzhe and Tay, Francis E. H. and Lim, Ser-Nam and Yang, Harry and Yuan, Li},
title = {DreamDance: Animating Human Images by Enriching 3D Geometry Cues from 2D Poses},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {14039-14050}
}
|
In this work, we present DreamDance, a novel method for animating human images using only skeleton pose sequences as conditional inputs. Existing approaches struggle with generating coherent, high-quality content in an efficient and user-friendly manner. Concretely, baseline methods relying on only 2D pose guidance lack the cues of 3D information like depth and normal maps, leading to suboptimal results. Other works introduce extra representations to provide additional 3D information but inevitably involve a cumbersome and time-intensive process. To address these limitations, DreamDance enriches 3D geometry cues from 2D poses by introducing an efficient diffusion model, enabling high-quality human image animation with various guidance. Our key insight is that human images naturally exhibit multiple levels of correlation, progressing from coarse skeleton poses to fine-grained geometry cues, and further from these geometry cues to explicit appearance details. Capturing such correlations could enrich the guidance signals, facilitating intra-frame coherency and inter-frame consistency. Specifically, we construct the TikTok-Dance5K dataset, comprising 5K high-quality dance videos with detailed frame annotations, including human pose, depth, and normal maps. Next, we introduce a Mutually Aligned Geometry Diffusion Model to generate fine-grained depth and normal maps for enriched guidance. Finally, a Cross-domain Controller incorporates multi-level guidance to animate human images effectively with a video diffusion model. Extensive experiments demonstrate that our method achieves state-of-the-art performance in animating human images compared to baseline methods.
|
2412.00397
| null | null | null |
[] |
[] |
[] |
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] |
312
|
MBTI: Masked Blending Transformers with Implicit Positional Encoding for Frame-rate Agnostic Motion Estimation
|
[
"Jungwoo Huh",
"Yeseung Park",
"Seongjean Kim",
"Jungsu Kim",
"Sanghoon Lee"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Huh_MBTI_Masked_Blending_Transformers_with_Implicit_Positional_Encoding_for_Frame-rate_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Huh_MBTI_Masked_Blending_Transformers_with_Implicit_Positional_Encoding_for_Frame-rate_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Huh_MBTI_Masked_Blending_ICCV_2025_supplemental.pdf
|
@InProceedings{Huh_2025_ICCV,
author = {Huh, Jungwoo and Park, Yeseung and Kim, Seongjean and Kim, Jungsu and Lee, Sanghoon},
title = {MBTI: Masked Blending Transformers with Implicit Positional Encoding for Frame-rate Agnostic Motion Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11568-11578}
}
|
Human motion estimation models typically assume a fixed number of input frames, making them sensitive to variations in frame rate and leading to inconsistent motion predictions across different temporal resolutions. This limitation arises because input frame rates inherently determine the temporal granularity of motion capture, causing discrepancies when models trained on a specific frame rate encounter different sampling frequencies. To address this challenge, we propose MBTI (Masked Blending Transformers with Implicit Positional Encoding), a frame rate-agnostic human motion estimation framework designed to maintain temporal consistency across varying input frame rates. Our approach leverages a masked autoencoder (MAE) architecture with masked token blending, which aligns input tokens with a predefined high-reference frame rate, ensuring a standardized temporal representation. Additionally, we introduce implicit positional encoding, which encodes absolute time information using neural implicit functions, enabling more natural motion reconstruction beyond discrete sequence indexing. By reconstructing motion at a high reference frame rate and optional downsampling, MBTI ensures both frame rate generalization and temporal consistency. To comprehensively evaluate MBTI, we introduce EMDB-FPS, an augmented benchmark designed to assess motion estimation robustness across multiple frame rates in both local and global motion estimation tasks. To further assess MBTI's robustness, we introduce the Motion Consistency across Frame rates (MCF), a novel metric to quantify the deviation of motion predictions across different input frame rates. Our results demonstrate that MBTI outperforms state-of-the-art methods in both motion accuracy and temporal consistency, achieving the most stable and consistent motion predictions across varying frame rates.
| null | null | null | null |
[] |
[] |
[] |
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] |
313
|
MixRI: Mixing Features of Reference Images for Novel Object Pose Estimation
|
[
"Xinhang Liu",
"Jiawei Shi",
"Zheng Dang",
"Yuchao Dai"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_MixRI_Mixing_Features_of_Reference_Images_for_Novel_Object_Pose_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_MixRI_Mixing_Features_of_Reference_Images_for_Novel_Object_Pose_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_MixRI_Mixing_Features_ICCV_2025_supplemental.pdf
|
@InProceedings{Liu_2025_ICCV,
author = {Liu, Xinhang and Shi, Jiawei and Dang, Zheng and Dai, Yuchao},
title = {MixRI: Mixing Features of Reference Images for Novel Object Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9024-9035}
}
|
We present MixRI, a lightweight network that solves the CAD-based novel object pose estimation problem in RGB images. It can be instantly applied to a novel object at test time without finetuning. We design our network to meet the demands of real-world applications, emphasizing reduced memory requirements and fast inference time. Unlike existing works that utilize many reference images and have large network parameters, we directly match points based on the multi-view information between the query and reference images with a lightweight network. Thanks to our reference image fusion strategy, we significantly decrease the number of reference images, thus decreasing the time needed to process these images and the memory required to store them. Furthermore, with our lightweight network, our method requires less inference time. Though with fewer reference images, experiments on seven core datasets in the BOP challenge show that our method achieves comparable results with other methods that require more reference images and larger network parameters.
| null | null | null | null |
[] |
[] |
[] |
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] |
314
|
p-AVAS: Can Physics-Integrated Audio-Visual Modeling Boost Neural Acoustic Synthesis?
|
[
"Susan Liang",
"Chao Huang",
"Yunlong Tang",
"Zeliang Zhang",
"Chenliang Xu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liang_p-AVAS_Can_Physics-Integrated_Audio-Visual_Modeling_Boost_Neural_Acoustic_Synthesis_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liang_p-AVAS_Can_Physics-Integrated_Audio-Visual_Modeling_Boost_Neural_Acoustic_Synthesis_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liang_p-AVAS_Can_Physics-Integrated_ICCV_2025_supplemental.pdf
|
@InProceedings{Liang_2025_ICCV,
author = {Liang, Susan and Huang, Chao and Tang, Yunlong and Zhang, Zeliang and Xu, Chenliang},
title = {p-AVAS: Can Physics-Integrated Audio-Visual Modeling Boost Neural Acoustic Synthesis?},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13942-13951}
}
|
The Audio-Visual Acoustic Synthesis (AVAS) task aims to model realistic audio propagation behavior within a specific visual scene. Prior works often rely on sparse image representations to guide acoustic synthesis. However, we argue that this approach is insufficient to capture the intricate physical properties of the environment and may struggle with generalization across diverse scenes. In this work, we review the limitations of existing pipelines and address the research question: Can we leverage physical audio-visual associations to enhance neural acoustic synthesis? We introduce Physics-Integrated Audio-Visual Acoustic Synthesis (PI-AVAS or \pi-AVAS), a novel framework designed with two key objectives. i) Generalization: We develop a vision-guided audio simulation framework that leverages physics-based sound propagation. By explicitly modeling vision-grounded geometry and sound rays, our approach achieves robust performance across diverse visual environments. ii) Realism: While simulation-based approaches offer generalizability, they often compromise on realism. To mitigate this, we incorporate a second stage for data-centric refinement, where we propose a flow matching-based audio refinement model to narrow the gap between simulation and real-world audio-visual scenes. Extensive experiments demonstrate the effectiveness and robustness of our method. We achieve state-of-the-art performance on the RWAVS-Gen, RWAVS, and RAF datasets. Additionally, we show that our approach can be seamlessly integrated with existing methods to significantly improve their performance.
| null | null | null | null |
[] |
[] |
[] |
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] |
315
|
Learning Precise Affordances from Egocentric Videos for Robotic Manipulation
|
[
"Gen Li",
"Nikolaos Tsagkas",
"Jifei Song",
"Ruaridh Mon-Williams",
"Sethu Vijayakumar",
"Kun Shao",
"Laura Sevilla-Lara"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_Learning_Precise_Affordances_from_Egocentric_Videos_for_Robotic_Manipulation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_Learning_Precise_Affordances_from_Egocentric_Videos_for_Robotic_Manipulation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_Learning_Precise_Affordances_ICCV_2025_supplemental.pdf
|
@InProceedings{Li_2025_ICCV,
author = {Li, Gen and Tsagkas, Nikolaos and Song, Jifei and Mon-Williams, Ruaridh and Vijayakumar, Sethu and Shao, Kun and Sevilla-Lara, Laura},
title = {Learning Precise Affordances from Egocentric Videos for Robotic Manipulation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10581-10591}
}
|
Affordance, defined as the potential actions that an object offers, is crucial for embodied AI agents. For example, such knowledge directs an agent to grasp a knife by the handle for cutting or by the blade for safe handover. While existing approaches have made notable progress, affordance research still faces three key challenges: data scarcity, poor generalization, and real-world deployment. Specifically, there is a lack of large-scale affordance datasets with precise segmentation maps, existing models struggle to generalize across different domains or novel object and affordance classes, and little work demonstrates deployability in real-world scenarios. In this work, we address these issues by proposing a complete affordance learning system that (1) takes in egocentric videos and outputs precise affordance annotations without human labeling, (2) leverages geometric information and vision foundation models to improve generalization, and (3) introduces a framework that facilitates affordance-oriented robotic manipulation such as tool grasping and robot-to-human tool handover. Experimental results show that our model surpasses the state-of-the-art by 13.8% in mIoU, and the framework achieves 77.1% successful grasping among 179 trials, including evaluations on seen, unseen classes, and cluttered scenes. Project page: https://reagan1311.github.io/affgrasp.
|
2408.10123
|
Project page: https://reagan1311.github.io/affgrasp
| null | null |
[] |
[] |
[] |
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] |
316
|
VAFlow: Video-to-Audio Generation with Cross-Modality Flow Matching
|
[
"Xihua Wang",
"Xin Cheng",
"Yuyue Wang",
"Ruihua Song",
"Yunfeng Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_VAFlow_Video-to-Audio_Generation_with_Cross-Modality_Flow_Matching_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_VAFlow_Video-to-Audio_Generation_with_Cross-Modality_Flow_Matching_ICCV_2025_paper.pdf
| null |
@InProceedings{Wang_2025_ICCV,
author = {Wang, Xihua and Cheng, Xin and Wang, Yuyue and Song, Ruihua and Wang, Yunfeng},
title = {VAFlow: Video-to-Audio Generation with Cross-Modality Flow Matching},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11777-11786}
}
|
Video-to-audio (V2A) generation aims to synthesize temporally aligned, realistic sounds for silent videos, a critical capability for immersive multimedia applications. Current V2A methods, predominantly based on diffusion or flow models, rely on suboptimal noise-to-audio paradigms that entangle cross-modal mappings with stochastic priors, resulting in inefficient training and convoluted transport paths. We propose VAFlow, a novel flow-based framework that directly models the video-to-audio transformation, eliminating reliance on noise priors. To address modality discrepancies, we employ an alignment variational autoencoder that compresses heterogeneous video features into audio-aligned latent spaces while preserving spatiotemporal semantics. By retaining cross-attention mechanisms between video features and flow blocks, our architecture enables classifier-free guidance within video source-driven generation. Without external data or complex training tricks, VAFlow achieves state-of-the-art performance on VGGSound benchmark, surpassing even text-augmented models in audio fidelity, diversity, and distribution alignment. This work establishes a new paradigm for V2A generation with a direct and effective video-to-audio transformation via flow matching.
| null | null | null | null |
[] |
[] |
[] |
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] |
317
|
HDR Image Generation via Gain Map Decomposed Diffusion
|
[
"Yuanshen Guan",
"Ruikang Xu",
"Yinuo Liao",
"Mingde Yao",
"Lizhi Wang",
"Zhiwei Xiong"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Guan_HDR_Image_Generation_via_Gain_Map_Decomposed_Diffusion_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Guan_HDR_Image_Generation_via_Gain_Map_Decomposed_Diffusion_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Guan_HDR_Image_Generation_ICCV_2025_supplemental.pdf
|
@InProceedings{Guan_2025_ICCV,
author = {Guan, Yuanshen and Xu, Ruikang and Liao, Yinuo and Yao, Mingde and Wang, Lizhi and Xiong, Zhiwei},
title = {HDR Image Generation via Gain Map Decomposed Diffusion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {17536-17545}
}
|
While diffusion models have demonstrated significant success in standard dynamic range (SDR) image synthesis, generating high dynamic range (HDR) images with higher luminance and broader color gamuts remains challenging. This arises primarily from two factors: (1) The incompatibility between pretrained SDR image auto-encoders and the high-bit-depth HDR images; (2) The lack of large-scale HDR image datasets for effective learning and supervision. In this paper, we propose a novel framework for HDR image generation with two key innovations: (1) Decomposed HDR Image Generation: We leverage a double-layer HDR image format to decompose the HDR image into two low-bit-depth components: an SDR image with a corresponding Gain Map (GM).This format is inherently compatible with pretrained SDR auto-encoders, motivating the decomposition of HDR image generation into SDR image and GM prediction. (2) Unsupervised Data Construction: We develop an automated pipeline to construct "Text-SDR-GM" triplets from large-scale text-image datasets by brightness-aware compression and gamut-constrained reduction, enabling unsupervised learning of GMs without ground-truth data. Building upon these innovations, we adapt the Stable Diffusion model to jointly predict GMs and SDR images, enabling high-quality decomposed HDR image generation. Experiments show that our framework excels in HDR image generation and SDR-to-HDRTV up-conversion, generalizing well across diverse scenes and conditions.
| null | null | null | null |
[] |
[] |
[] |
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] |
318
|
DiffuMatch: Category-Agnostic Spectral Diffusion Priors for Robust Non-rigid Shape Matching
|
[
"Emery Pierson",
"Lei Li",
"Angela Dai",
"Maks Ovsjanikov"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Pierson_DiffuMatch_Category-Agnostic_Spectral_Diffusion_Priors_for_Robust_Non-rigid_Shape_Matching_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Pierson_DiffuMatch_Category-Agnostic_Spectral_Diffusion_Priors_for_Robust_Non-rigid_Shape_Matching_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Pierson_DiffuMatch_Category-Agnostic_Spectral_ICCV_2025_supplemental.pdf
|
@InProceedings{Pierson_2025_ICCV,
author = {Pierson, Emery and Li, Lei and Dai, Angela and Ovsjanikov, Maks},
title = {DiffuMatch: Category-Agnostic Spectral Diffusion Priors for Robust Non-rigid Shape Matching},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {5745-5756}
}
|
Deep functional maps have recently emerged as a powerful tool for solving non-rigid shape correspondence tasks. Methods that use this approach combine the power and flexibility of the functional map framework, with data-driven learning for improved accuracy and generality. However, most existing methods in this area restrict the learning aspect only to the feature functions and still rely on axiomatic modeling for formulating the training loss or for functional map regularization inside the networks. This limits both the accuracy and the applicability of the resulting approaches only to scenarios where assumptions of the axiomatic models hold. In this work, we show, for the first time, that both in-network regularization and functional map training can be replaced with data-driven methods. For this, we first train a generative model of functional maps in the spectral domain using score-based generative modeling, built from a large collection of high-quality maps. We then exploit the resulting model to promote the structural properties of ground truth functional maps on new shape collections. Remarkably, we demonstrate that the learned models are category-agnostic, and can fully replace commonly used strategies such as enforcing Laplacian commutativity or orthogonality of functional maps. Our key technical contribution is a novel distillation strategy from diffusion models in the spectral domain. Experiments demonstrate that our learned regularization leads to better results than axiomatic approaches for zero-shot non-rigid shape matching. Our code is available at: https://github.com/daidedou/diffumatch/
|
2507.23715
|
Presented at ICCV 2025
|
https://github.com/daidedou/diffumatch
| null |
[] |
[] |
[] |
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] |
319
|
MoGA: 3D Generative Avatar Prior for Monocular Gaussian Avatar Reconstruction
|
[
"Zijian Dong",
"Longteng Duan",
"Jie Song",
"Michael J. Black",
"Andreas Geiger"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Dong_MoGA_3D_Generative_Avatar_Prior_for_Monocular_Gaussian_Avatar_Reconstruction_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Dong_MoGA_3D_Generative_Avatar_Prior_for_Monocular_Gaussian_Avatar_Reconstruction_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Dong_MoGA_3D_Generative_ICCV_2025_supplemental.pdf
|
@InProceedings{Dong_2025_ICCV,
author = {Dong, Zijian and Duan, Longteng and Song, Jie and Black, Michael J. and Geiger, Andreas},
title = {MoGA: 3D Generative Avatar Prior for Monocular Gaussian Avatar Reconstruction},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13304-13314}
}
|
We present MoGA, a novel method to reconstruct high-fidelity 3D Gaussian avatars from a single-view image. The main challenge lies in inferring unseen appearance and geometric details while ensuring 3D consistency and realism. Most previous methods rely on 2D diffusion models to synthesize unseen views; however, these generated views are sparse and inconsistent, resulting in unrealistic 3D artifacts and blurred appearance. To address these limitations, we leverage a generative avatar model, that can generate diverse 3D avatars by sampling deformed Gaussians from a learned prior distribution. Due to limited 3D training data, such a 3D model alone cannot capture all image details of unseen identities. Consequently, we integrate it as a prior, ensuring 3D consistency by projecting input images into its latent space and enforcing additional 3D appearance and geometric constraints. Our novel approach formulates Gaussian avatar creation as model inversion by fitting the generative avatar to synthetic views from 2D diffusion models. The generative avatar provides an initialization for model fitting, enforces 3D regularization, and helps in refining pose. Experiments show that our method surpasses state-of-the-art techniques and generalizes well to real-world scenarios. Our Gaussian avatars are also inherently animatable. For code, see https:// zj-dong.github.io/ MoGA/
|
2507.23597
|
ICCV 2025 (Highlight), Project Page: https://zj-dong.github.io/MoGA/
| null | null |
[] |
[] |
[] |
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] |
320
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Aligning Moments in Time using Video Queries
|
[
"Yogesh Kumar",
"Uday Agarwal",
"Manish Gupta",
"Anand Mishra"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Kumar_Aligning_Moments_in_Time_using_Video_Queries_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Kumar_Aligning_Moments_in_Time_using_Video_Queries_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Kumar_Aligning_Moments_in_ICCV_2025_supplemental.pdf
|
@InProceedings{Kumar_2025_ICCV,
author = {Kumar, Yogesh and Agarwal, Uday and Gupta, Manish and Mishra, Anand},
title = {Aligning Moments in Time using Video Queries},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {20215-20225}
}
|
Video-to-video moment retrieval (Vid2VidMR) is the task of localizing unseen events or moments in a target video using a query video. This task poses several challenges, such as the need for semantic frame-level alignment and modeling complex dependencies between query and target videos. To tackle this challenging problem, we introduce MATR (Moment Alignment TRansformer), a transformer-based model designed to capture semantic context as well as the temporal details necessary for precise moment localization. MATR conditions target video representations on query video features using dual-stage sequence alignment that encodes the required correlations and dependencies. These representations are then used to guide foreground/background classification and boundary prediction heads, enabling the model to accurately identify moments in the target video that semantically match with the query video. Additionally, to provide a strong task-specific initialization for MATR, we propose a self-supervised pre-training technique that involves training the model to localize random clips within videos. Extensive experiments demonstrate that MATR achieves notable performance improvements of 13.1% in R@1 and 8.1% in mIoU on an absolute scale compared to state-of-the-art methods on the popular ActivityNet-VRL dataset. Additionally, on our newly proposed dataset, SportsMoments, MATR shows a 14.7% gain in R@1 and a 14.4% gain in mIoU on an absolute scale over strong baselines.
|
2508.15439
|
11 pages, 4 figures, accepted at ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
321
|
StreamGS: Online Generalizable Gaussian Splatting Reconstruction for Unposed Image Streams
|
[
"Yang Li",
"Jinglu Wang",
"Lei Chu",
"Xiao Li",
"Shiu-Hong Kao",
"Ying-Cong Chen",
"Yan Lu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_StreamGS_Online_Generalizable_Gaussian_Splatting_Reconstruction_for_Unposed_Image_Streams_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_StreamGS_Online_Generalizable_Gaussian_Splatting_Reconstruction_for_Unposed_Image_Streams_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_StreamGS_Online_Generalizable_ICCV_2025_supplemental.zip
|
@InProceedings{Li_2025_ICCV,
author = {Li, Yang and Wang, Jinglu and Chu, Lei and Li, Xiao and Kao, Shiu-Hong and Chen, Ying-Cong and Lu, Yan},
title = {StreamGS: Online Generalizable Gaussian Splatting Reconstruction for Unposed Image Streams},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {25841-25850}
}
|
The advent of 3D Gaussian Splatting (3DGS) has advanced 3D scene reconstruction and novel view synthesis. With the growing interest of interactive applications that need immediate feedback, online 3DGS reconstruction in real-time is in high demand. However, none of existing methods yet meet the demand due to three main challenges: the absence of predetermined camera parameters, the need for generalizable 3DGS optimization, and the necessity of reducing redundancy. We propose StreamGS, an online generalizable 3DGS reconstruction method for unposed image streams, which progressively transform image streams to 3D Gaussian streams by predicting and aggregating per-frame Gaussians. Our method overcomes the limitation of the initial point reconstruction in tackling out-of-domain (OOD) issues by introducing a content adaptive refinement. The refinement enhances cross-frame consistency by establishing reliable pixel correspondences between adjacent frames. Such correspondences further aid in merging redundant Gaussians through cross-frame feature aggregation. The density of Gaussians is thereby reduced, empowering online reconstruction by significantly lowering computational and memory costs. Extensive experiments on diverse datasets have demonstrated that StreamGS achieves quality on par with optimization-based approaches but does so 150 times faster, and exhibits superior generalizability in handling OOD scenes.
|
2503.06235
|
8 pages
| null | null |
[] |
[] |
[] |
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] |
322
|
Adversarial Attention Perturbations for Large Object Detection Transformers
|
[
"Zachary Yahn",
"Selim Furkan Tekin",
"Fatih Ilhan",
"Sihao Hu",
"Tiansheng Huang",
"Yichang Xu",
"Margaret Loper",
"Ling Liu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yahn_Adversarial_Attention_Perturbations_for_Large_Object_Detection_Transformers_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yahn_Adversarial_Attention_Perturbations_for_Large_Object_Detection_Transformers_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yahn_Adversarial_Attention_Perturbations_ICCV_2025_supplemental.pdf
|
@InProceedings{Yahn_2025_ICCV,
author = {Yahn, Zachary and Tekin, Selim Furkan and Ilhan, Fatih and Hu, Sihao and Huang, Tiansheng and Xu, Yichang and Loper, Margaret and Liu, Ling},
title = {Adversarial Attention Perturbations for Large Object Detection Transformers},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {3184-3193}
}
|
Adversarial perturbations are useful tools for exposing vulnerabilities in neural networks. Existing adversarial perturbation methods for object detection are either limited to attacking CNN-based detectors or weak against transformer-based detectors. This paper presents an Attention-Focused Offensive Gradient (AFOG) attack against object detection transformers. By design, AFOG is neural-architecture agnostic and effective for attacking both large transformer-based object detectors and conventional CNN-based detectors with a unified adversarial attention framework. This paper makes three original contributions. First, AFOG utilizes a learnable attention mechanism that focuses perturbations on vulnerable image regions in multi-box detection tasks, increasing performance over non-attention baselines by up to 30.6%. Second, AFOG's attack loss is formulated by integrating two types of feature loss through learnable attention updates with iterative injection of adversarial perturbations. Finally, AFOG is an efficient and stealthy adversarial perturbation method. It probes the weak spots of detection transformers by adding strategically generated and visually imperceptible perturbations which can cause well-trained object detection models to fail. Extensive experiments conducted with twelve large detection transformers on COCO demonstrate the efficacy of AFOG. Our empirical results also show that AFOG outperforms existing attacks on transformer-based and CNN-based object detectors by up to 83% with superior speed and imperceptibility. Code is available at: https://github.com/zacharyyahn/AFOG
|
2508.02987
|
ICCV 2025
|
https://github.com/zacharyyahn/AFOG
| null |
[] |
[] |
[] |
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] |
323
|
Meta-Learning Dynamic Center Distance: Hard Sample Mining for Learning with Noisy Labels
|
[
"Chenyu Mu",
"Yijun Qu",
"Jiexi Yan",
"Erkun Yang",
"Cheng Deng"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Mu_Meta-Learning_Dynamic_Center_Distance_Hard_Sample_Mining_for_Learning_with_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Mu_Meta-Learning_Dynamic_Center_Distance_Hard_Sample_Mining_for_Learning_with_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Mu_Meta-Learning_Dynamic_Center_ICCV_2025_supplemental.pdf
|
@InProceedings{Mu_2025_ICCV,
author = {Mu, Chenyu and Qu, Yijun and Yan, Jiexi and Yang, Erkun and Deng, Cheng},
title = {Meta-Learning Dynamic Center Distance: Hard Sample Mining for Learning with Noisy Labels},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {415-425}
}
|
The sample selection approach is a widely adopted strategy for learning with noisy labels, where examples with lower losses are effectively treated as clean during training. However, this clean set often becomes dominated by easy examples, limiting the model's meaningful exposure to more challenging cases and reducing its expressive power. To overcome this limitation, we introduce a novel metric called Dynamic Center Distance (DCD), which can quantify sample difficulty and provide information that critically complements loss values. Unlike approaches that rely on predictions, DCD is computed in feature space as the distance between sample features and a dynamically updated center, established through a proposed meta-learning framework. Building on preliminary semi-supervised training that captures fundamental data patterns, we incorporate DCD to further refine the classification loss, down-weighting well-classified examples and strategically focusing training on a sparse set of hard instances. This strategy prevents easy examples from dominating the classifier, leading to more robust learning. Extensive experiments across multiple benchmark datasets, including synthetic and real-world noise settings, as well as natural and medical images, consistently demonstrate the effectiveness of our method.
| null | null | null | null |
[] |
[] |
[] |
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] |
324
|
Ensemble Foreground Management for Unsupervised Object Discovery
|
[
"Ziling Wu",
"Armaghan Moemeni",
"Praminda Caleb-Solly"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wu_Ensemble_Foreground_Management_for_Unsupervised_Object_Discovery_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wu_Ensemble_Foreground_Management_for_Unsupervised_Object_Discovery_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wu_Ensemble_Foreground_Management_ICCV_2025_supplemental.pdf
|
@InProceedings{Wu_2025_ICCV,
author = {Wu, Ziling and Moemeni, Armaghan and Caleb-Solly, Praminda},
title = {Ensemble Foreground Management for Unsupervised Object Discovery},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {20268-20279}
}
|
Unsupervised object discovery (UOD) aims to detect and segment objects in 2D images without handcrafted annotations. Recent progress in self-supervised representation learning has led to some success in UOD algorithms. However, the absence of ground truth provides existing UOD methods with two challenges: 1) determining if a discovered region is foreground or background, and 2) knowing how many objects remain undiscovered. To address these two problems, previous solutions rely on foreground priors to distinguish if the discovered region is foreground, and conduct one or fixed iterations of discovery. However, the existing foreground priors are heuristic and not always robust, and a fixed number of discoveries leads to under or over-segmentation, since the number of objects in images varies. This paper introduces UnionCut, a robust and well-grounded foreground prior based on min-cut and ensemble methods that detects the union of foreground areas of an image, allowing UOD algorithms to identify foreground objects and stop discovery once the majority of the foreground union in the image is segmented. In addition, we propose UnionSeg, a distilled transformer of UnionCut that outputs the foreground union more efficiently and accurately. Our experiments show that by combining with UnionCut or UnionSeg, previous state-of-the-art UOD methods witness an increase in the performance of single object discovery, saliency detection and self-supervised instance segmentation on various benchmarks. The code is available at https://github.com/YFaris/UnionCut.
|
2507.20860
|
Accepted by ICCV2025 (Highlight)
|
https://github.com/YFaris/UnionCut
| null |
[] |
[] |
[] |
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] |
325
|
RESCUE: Crowd Evacuation Simulation via Controlling SDM-United Characters
|
[
"Xiaolin Liu",
"Tianyi Zhou",
"Hongbo Kang",
"Jian Ma",
"Ziwen Wang",
"Jing Huang",
"Wenguo Weng",
"Yu-Kun Lai",
"Kun Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_RESCUE_Crowd_Evacuation_Simulation_via_Controlling_SDM-United_Characters_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_RESCUE_Crowd_Evacuation_Simulation_via_Controlling_SDM-United_Characters_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_RESCUE_Crowd_Evacuation_ICCV_2025_supplemental.zip
|
@InProceedings{Liu_2025_ICCV,
author = {Liu, Xiaolin and Zhou, Tianyi and Kang, Hongbo and Ma, Jian and Wang, Ziwen and Huang, Jing and Weng, Wenguo and Lai, Yu-Kun and Li, Kun},
title = {RESCUE: Crowd Evacuation Simulation via Controlling SDM-United Characters},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24955-24964}
}
|
Crowd evacuation simulation is critical for enhancing public safety, and demanded for realistic virtual environments. Current mainstream evacuation models overlook the complex human behaviors that occur during evacuation, such as pedestrian collisions, interpersonal interactions, and variations in behavior influenced by terrain types or individual body shapes. This results in the failure to accurately simulate the escape of people in the real world. In this paper, aligned with the sensory-decision-motor (SDM) flow of the human brain, we propose a real-time 3D crowd evacuation simulation framework that integrates a 3D-adaptive SFM (Social Force Model) Decision Mechanism and a Personalized Gait Control Motor. This framework allows multiple agents to move in parallel and is suitable for various scenarios, with dynamic crowd awareness. Additionally, we introduce Part-level Force Visualization to assist in evacuation analysis. Experimental results demonstrate that our framework supports dynamic trajectory planning and personalized behavior for each agent throughout the evacuation process, and is compatible with uneven terrain. Visually, our method generates evacuation results that are more realistic and plausible, providing enhanced insights for crowd simulation. The code is available at http://cic.tju.edu.cn/faculty/likun/projects/RESCUE.
|
2507.20117
| null | null |
https://cic.tju.edu.cn/faculty/likun/projects/RESCUE
|
[] |
[] |
[] |
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] |
326
|
Joint Diffusion Models in Continual Learning
|
[
"Paweł Skierś",
"Kamil Deja"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Skiers_Joint_Diffusion_Models_in_Continual_Learning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Skiers_Joint_Diffusion_Models_in_Continual_Learning_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Skiers_Joint_Diffusion_Models_ICCV_2025_supplemental.zip
|
@InProceedings{Skiers_2025_ICCV,
author = {Skier\'s, Pawe{\l} and Deja, Kamil},
title = {Joint Diffusion Models in Continual Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4380-4390}
}
|
In this work, we introduce JDCL - a new method for continual learning with generative rehearsal based on joint diffusion models. Neural networks suffer from catastrophic forgetting defined as abrupt loss in the model's performance when retrained with additional data coming from a different distribution. Generative-replay-based continual learning methods try to mitigate this issue by retraining a model with a combination of new and rehearsal data sampled from a generative model. In this work, we propose to extend this idea by combining a continually trained classifier with a diffusion-based generative model into a single - jointly optimized neural network. We show that such shared parametrization, combined with the knowledge distillation technique allows for stable adaptation to new tasks without catastrophic forgetting. We evaluate our approach on several benchmarks, where it outperforms recent state-of-the-art generative replay techniques. Additionally, we extend our method to the semi-supervised continual learning setup, where it outperforms competing buffer-based replay techniques, and evaluate, in a self-supervised manner, the quality of trained representations.
|
2411.08224
| null | null | null |
[] |
[] |
[] |
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] |
327
|
MissRAG: Addressing the Missing Modality Challenge in Multimodal Large Language Models
|
[
"Vittorio Pipoli",
"Alessia Saporita",
"Federico Bolelli",
"Marcella Cornia",
"Lorenzo Baraldi",
"Costantino Grana",
"Rita Cucchiara",
"Elisa Ficarra"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Pipoli_MissRAG_Addressing_the_Missing_Modality_Challenge_in_Multimodal_Large_Language_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Pipoli_MissRAG_Addressing_the_Missing_Modality_Challenge_in_Multimodal_Large_Language_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Pipoli_MissRAG_Addressing_the_ICCV_2025_supplemental.pdf
|
@InProceedings{Pipoli_2025_ICCV,
author = {Pipoli, Vittorio and Saporita, Alessia and Bolelli, Federico and Cornia, Marcella and Baraldi, Lorenzo and Grana, Costantino and Cucchiara, Rita and Ficarra, Elisa},
title = {MissRAG: Addressing the Missing Modality Challenge in Multimodal Large Language Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {3215-3224}
}
|
Recently, Multimodal Large Language Models (MLLMs) have emerged as a leading framework for enhancing the ability of Large Language Models (LLMs) to interpret non-linguistic modalities. Despite their impressive capabilities, the robustness of MLLMs under conditions where one or more modalities are missing remains largely unexplored. In this paper, we investigate the extent to which MLLMs can maintain performance when faced with missing modality inputs. Moreover, we propose a novel framework to mitigate the aforementioned issue called retrieval-augmented generation for missing modalities (MissRAG). It consists of a novel multimodal RAG technique alongside a tailored prompt engineering strategy designed to enhance model robustness by mitigating the impact of absent modalities while preventing the burden of additional instruction tuning. To demonstrate the effectiveness of our techniques, we conduct comprehensive evaluations across five diverse datasets, covering tasks such as audio-visual question answering, audio-visual captioning, and multimodal sentiment analysis. Our source code is available at https://github.com/aimagelab/MissRAG.
| null | null | null | null |
[] |
[] |
[] |
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] |
328
|
Signs as Tokens: A Retrieval-Enhanced Multilingual Sign Language Generator
|
[
"Ronglai Zuo",
"Rolandos Alexandros Potamias",
"Evangelos Ververas",
"Jiankang Deng",
"Stefanos Zafeiriou"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zuo_Signs_as_Tokens_A_Retrieval-Enhanced_Multilingual_Sign_Language_Generator_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zuo_Signs_as_Tokens_A_Retrieval-Enhanced_Multilingual_Sign_Language_Generator_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zuo_Signs_as_Tokens_ICCV_2025_supplemental.pdf
|
@InProceedings{Zuo_2025_ICCV,
author = {Zuo, Ronglai and Potamias, Rolandos Alexandros and Ververas, Evangelos and Deng, Jiankang and Zafeiriou, Stefanos},
title = {Signs as Tokens: A Retrieval-Enhanced Multilingual Sign Language Generator},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {23806-23816}
}
|
Sign language is a visual language that encompasses all linguistic features of natural languages and serves as the primary communication method for the deaf and hard-of-hearing communities. Although many studies have successfully adapted pretrained language models (LMs) for sign language translation (sign-to-text), the reverse task--sign language generation (text-to-sign)--remains largely unexplored. In this work, we introduce a multilingual sign language model, Signs as Tokens (SOKE), which can generate 3D sign avatars autoregressively from text inputs using a pretrained LM. To align sign language with the LM, we leverage a decoupled tokenizer that discretizes continuous signs into token sequences representing various body parts. During decoding, unlike existing approaches that flatten all part-wise tokens into a single sequence and predict one token at a time, we propose a multi-head decoding method capable of predicting multiple tokens simultaneously. This approach improves inference efficiency while maintaining effective information fusion across different body parts. To further ease the generation process, we propose a retrieval-enhanced SLG approach, which incorporates external sign dictionaries to provide accurate word-level signs as auxiliary conditions, significantly improving the precision of generated signs. Extensive qualitative and quantitative evaluations demonstrate the effectiveness of SOKE.
|
2411.17799
|
Accepted by ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
329
|
Test-Time Prompt Tuning for Zero-Shot Depth Completion
|
[
"Chanhwi Jeong",
"Inhwan Bae",
"Jin-Hwi Park",
"Hae-Gon Jeon"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Jeong_Test-Time_Prompt_Tuning_for_Zero-Shot_Depth_Completion_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Jeong_Test-Time_Prompt_Tuning_for_Zero-Shot_Depth_Completion_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Jeong_Test-Time_Prompt_Tuning_ICCV_2025_supplemental.pdf
|
@InProceedings{Jeong_2025_ICCV,
author = {Jeong, Chanhwi and Bae, Inhwan and Park, Jin-Hwi and Jeon, Hae-Gon},
title = {Test-Time Prompt Tuning for Zero-Shot Depth Completion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9443-9454}
}
|
Zero-shot depth completion with metric scales poses significant challenges, primarily due to performance limitations such as domain specificity and sensor characteristics. One recent emerging solution is to integrate monocular depth foundation models into depth completion frameworks, yet these efforts still face issues with suboptimal performance and often require further adaptation to the target task. Surprisingly, we find that a simple test-time training, which fine-tunes monocular depth foundation models on sparse depth measurements from sensors just as it is, yields reasonable results. However, this test-time training obviously incurs high computational costs and introduces biases towards specific conditions, making it impractical for real-world scenarios. In this paper, we introduce a new approach toward parameter-efficient zero-shot depth completion. Our key idea of this work is to leverage visual prompt tuning, achieving sensor-specific depth scale adaptation without forgetting foundational knowledge. Experimental results on diverse datasets demonstrate that our approach outperforms relevant state-of-the-art methods, showing superior generalization and efficiency. Our source code is available in the supplementary materials.
| null | null | null | null |
[] |
[] |
[] |
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] |
330
|
GUIOdyssey: A Comprehensive Dataset for Cross-App GUI Navigation on Mobile Devices
|
[
"Quanfeng Lu",
"Wenqi Shao",
"Zitao Liu",
"Lingxiao Du",
"Fanqing Meng",
"Boxuan Li",
"Botong Chen",
"Siyuan Huang",
"Kaipeng Zhang",
"Ping Luo"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Lu_GUIOdyssey_A_Comprehensive_Dataset_for_Cross-App_GUI_Navigation_on_Mobile_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Lu_GUIOdyssey_A_Comprehensive_Dataset_for_Cross-App_GUI_Navigation_on_Mobile_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Lu_GUIOdyssey_A_Comprehensive_ICCV_2025_supplemental.pdf
|
@InProceedings{Lu_2025_ICCV,
author = {Lu, Quanfeng and Shao, Wenqi and Liu, Zitao and Du, Lingxiao and Meng, Fanqing and Li, Boxuan and Chen, Botong and Huang, Siyuan and Zhang, Kaipeng and Luo, Ping},
title = {GUIOdyssey: A Comprehensive Dataset for Cross-App GUI Navigation on Mobile Devices},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {22404-22414}
}
|
Autonomous Graphical User Interface (GUI) navigation agents can enhance user experience in communication, entertainment, and productivity by streamlining workflows and reducing manual intervention. However, prior GUI agents often trained with datasets comprising tasks that can be completed within a single app, leading to poor performance in cross-app navigation. To address this problem, we present GUIOdyssey, a comprehensive dataset for cross-app mobile GUI navigation. GUIOdyssey comprises 8,334 episodes with an average of 15.3 steps per episode, covering 6 mobile devices, 212 distinct apps, and 1,357 app combinations. Each step is enriched with detailed semantic reasoning annotations, which aid the model in building cognitive processes and enhancing its reasoning abilities for complex cross-app tasks. Building on GUIOdyssey, we develop OdysseyAgent, an exploratory multimodal agent for long-step cross-app navigation equipped with a history resampler module that efficiently attends to historical screenshot tokens, balancing performance and inference speed. Extensive experiments conducted in both in-domain and out-of-domain scenarios validate the effectiveness of our approach. Moreover, we demonstrate that historial information involving actions, screenshots and context in our dataset can significantly enhances OdysseyAgent's performance on complex cross-app tasks.
|
2406.08451
|
22 pages, 14 figures, ICCV 2025, a cross-app GUI navigation dataset
| null | null |
[] |
[] |
[
"OpenGVLab/GUI-Odyssey"
] |
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331
|
TOTP: Transferable Online Pedestrian Trajectory Prediction with Temporal-Adaptive Mamba Latent Diffusion
|
[
"Ziyang Ren",
"Ping Wei",
"Shangqi Deng",
"Haowen Tang",
"Jiapeng Li",
"Huan Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ren_TOTP_Transferable_Online_Pedestrian_Trajectory_Prediction_with_Temporal-Adaptive_Mamba_Latent_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ren_TOTP_Transferable_Online_Pedestrian_Trajectory_Prediction_with_Temporal-Adaptive_Mamba_Latent_ICCV_2025_paper.pdf
| null |
@InProceedings{Ren_2025_ICCV,
author = {Ren, Ziyang and Wei, Ping and Deng, Shangqi and Tang, Haowen and Li, Jiapeng and Li, Huan},
title = {TOTP: Transferable Online Pedestrian Trajectory Prediction with Temporal-Adaptive Mamba Latent Diffusion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26263-26272}
}
|
Pedestrian trajectory prediction is crucial for many intelligent tasks. While existing methods predict future trajectories from fixed-frame historical observations, they are limited by the observational perspective and the need for extensive historical information, resulting in prediction delays and inflexible generalization in real-time systems. In this paper, we propose a novel task called Transferable Online Pedestrian Trajectory Prediction (TOTP), which synchronously predicts future trajectories with variable observations and enables effective task transfer under different observation constraints. To advance TOTP modeling, we propose a Temporal-Adaptive Mamba Latent Diffusion (TAMLD) model. It utilizes the Social-Implicit Mamba Synthesizer to extract motion states with social interaction and refine temporal representations through Temporal-Aware Distillation. A Trend-Conditional Mamba Decomposer generates the motion latent distribution of the future motion trends and predicts future motion trajectories through sampling decomposition. We utilize Motion-Latent Mamba Diffusion to reconstruct the latent space disturbed by imbalanced temporal noise. Our method achieves state-of-the-art results on multiple datasets and tasks, showcasing temporal adaptability and generalization ability.
| null | null | null | null |
[] |
[] |
[] |
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332
|
AstroLoc: Robust Space to Ground Image Localizer
|
[
"Gabriele Berton",
"Alex Stoken",
"Carlo Masone"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Berton_AstroLoc_Robust_Space_to_Ground_Image_Localizer_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Berton_AstroLoc_Robust_Space_to_Ground_Image_Localizer_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Berton_AstroLoc_Robust_Space_ICCV_2025_supplemental.pdf
|
@InProceedings{Berton_2025_ICCV,
author = {Berton, Gabriele and Stoken, Alex and Masone, Carlo},
title = {AstroLoc: Robust Space to Ground Image Localizer},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {5811-5820}
}
|
Thousands of photos of Earth are taken every day by astronauts from the International Space Station. Localizing these photos, which has been performed manually for decades, has recently been approached through image retrieval solutions: given an astronaut photo, the goal is to find its most similar match among a large database of geo-tagged satellite images, in a task called Astronaut Photography Localization (APL). Yet, existing APL approaches are trained only using satellite images, without taking advantage of the millions of open-source astronaut photos. In this work we present the first APL pipeline capable of leveraging astronaut photos for training. We first produce full localization information for 300,000 manually weakly-labeled astronaut photos through an automated pipeline, and then use these images to train a model, called AstroLoc. AstroLoc learns a robust representation of Earth's surface features through two objective functions: pairing astronaut photos with their matching satellite counterparts in a pairwise loss, and a second loss on clusters of satellite imagery weighted by their relevance to astronaut photography through unsupervised mining. AstroLoc achieves a staggering 35% average improvement in recall@1 over previous SOTA, reaching a recall@100 consistently over 99% for existing datasets. Moreover, without fine-tuning, AstroLoc provides excellent results for related tasks like the lost-in-space satellite problem and historical space imagery localization.
|
2502.07003
|
https://astro-loc.github.io/
| null | null |
[] |
[] |
[] |
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] |
333
|
Dual Recursive Feedback on Generation and Appearance Latents for Pose-Robust Text-to-Image Diffusion
|
[
"Jiwon Kim",
"Pureum Kim",
"SeonHwa Kim",
"Soobin Park",
"Eunju Cha",
"Kyong Hwan Jin"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Kim_Dual_Recursive_Feedback_on_Generation_and_Appearance_Latents_for_Pose-Robust_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Kim_Dual_Recursive_Feedback_on_Generation_and_Appearance_Latents_for_Pose-Robust_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Kim_Dual_Recursive_Feedback_ICCV_2025_supplemental.pdf
|
@InProceedings{Kim_2025_ICCV,
author = {Kim, Jiwon and Kim, Pureum and Kim, SeonHwa and Park, Soobin and Cha, Eunju and Jin, Kyong Hwan},
title = {Dual Recursive Feedback on Generation and Appearance Latents for Pose-Robust Text-to-Image Diffusion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {15491-15500}
}
|
Recent advancements in controllable text-to-image (T2I) diffusion models, such as Ctrl-X and FreeControl, have demonstrated robust spatial and appearance control without requiring auxiliary module training. However, these models often struggle to accurately preserve spatial structures and fail to capture fine-grained conditions related to object poses and scene layouts. To address these challenges, we propose a training-free Dual Recursive Feedback (DRF) system that properly reflects control conditions in controllable T2I models. The proposed DRF consists of appearance feedback and generation feedback that recursively refines the intermediate latents to better reflect the given appearance information and the user's intent.This dual-update mechanism guides latent representations toward reliable manifolds, effectively integrating structural and appearance attributes. Our approach enables fine-grained generation even between class-invariant structure-appearance fusion, such as transferring human motion onto a tiger's form. Extensive experiments demonstrate the efficacy of our method in producing high-quality, semantically coherent, and structurally consistent image generations. Our source code is available at https://github.com/jwonkm/DRF.
|
2508.09575
| null |
https://github.com/jwonkm/DRF
| null |
[] |
[] |
[] |
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] |
334
|
Chimera: Improving Generalist Model with Domain-Specific Experts
|
[
"Tianshuo Peng",
"Mingsheng Li",
"Jiakang Yuan",
"Hongbin Zhou",
"Renqiu Xia",
"Renrui Zhang",
"Lei Bai",
"Song Mao",
"Bin Wang",
"Aojun Zhou",
"Botian Shi",
"Tao Chen",
"Bo Zhang",
"Xiangyu Yue"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Peng_Chimera_Improving_Generalist_Model_with_Domain-Specific_Experts_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Peng_Chimera_Improving_Generalist_Model_with_Domain-Specific_Experts_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Peng_Chimera_Improving_Generalist_ICCV_2025_supplemental.pdf
|
@InProceedings{Peng_2025_ICCV,
author = {Peng, Tianshuo and Li, Mingsheng and Yuan, Jiakang and Zhou, Hongbin and Xia, Renqiu and Zhang, Renrui and Bai, Lei and Mao, Song and Wang, Bin and Zhou, Aojun and Shi, Botian and Chen, Tao and Zhang, Bo and Yue, Xiangyu},
title = {Chimera: Improving Generalist Model with Domain-Specific Experts},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {3011-3022}
}
|
Large Multi-modal Models (LMMs), trained on web-scale datasets predominantly composed of natural images, have demonstrated remarkable performance on general tasks. However, these models often exhibit limited specialized capabilities for domain-specific tasks that require extensive domain prior knowledge. An intuitive solution is to post-train LMMs on a specific domain, but often suffers from the labor-intensive annotating process and the inaccessibility of private training data. Directly integrating expert models tailored for those tasks is also challenging due to representational gaps and imbalanced optimization. To address these challenges, we introduce Chimera, a scalable and low-cost multi-modal pipeline designed to boost the ability of existing LMMs with domain-specific experts. Specifically, we design a progressive training strategy to integrate features from expert models into the input of a generalist LMM. To address the imbalanced optimization caused by the well-aligned general visual encoder, we introduce a novel Generalist-Specialist Collaboration Masking (GSCM) mechanism. This results in a versatile model that excels across the chart, table, math, and document domains, achieving state-of-the-art performance on multi-modal reasoning and visual content extraction tasks, both of which are challenging tasks for assessing existing LMMs. We will release model weights, along with the data used for training and evaluation, to facilitate future research on LMMs.
|
2412.05983
|
Accepted by ICCV-2025, Chimera Homepage:
https://alpha-innovator.github.io/chimera_page
| null | null |
[] |
[
"U4R/Chimera-Reasoner-2B"
] |
[] |
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] |
335
|
EgoM2P: Egocentric Multimodal Multitask Pretraining
|
[
"Gen Li",
"Yutong Chen",
"Yiqian Wu",
"Kaifeng Zhao",
"Marc Pollefeys",
"Siyu Tang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_EgoM2P_Egocentric_Multimodal_Multitask_Pretraining_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_EgoM2P_Egocentric_Multimodal_Multitask_Pretraining_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_EgoM2P_Egocentric_Multimodal_ICCV_2025_supplemental.pdf
|
@InProceedings{Li_2025_ICCV,
author = {Li, Gen and Chen, Yutong and Wu, Yiqian and Zhao, Kaifeng and Pollefeys, Marc and Tang, Siyu},
title = {EgoM2P: Egocentric Multimodal Multitask Pretraining},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10830-10843}
}
|
Understanding multimodal signals in egocentric vision, such as RGB video, depth, camera poses, and gaze, is essential for applications in augmented reality, robotics, and human-computer interaction, enabling systems to better interpret the camera wearer's actions, intentions, and surrounding environment. However, building large-scale egocentric multimodal and multitask models presents unique challenges. Egocentric data are inherently heterogeneous, with large variations in modality coverage across devices and settings. Generating pseudo-labels for missing modalities, such as gaze or head-mounted camera trajectories, is often infeasible, making standard supervised learning approaches difficult to scale. Furthermore, dynamic camera motion and the complex temporal and spatial structure of first-person video pose additional challenges for the direct application of existing multimodal foundation models. To address these challenges, we introduce a set of efficient temporal tokenizers and propose EgoM2P, a masked modeling framework that learns from temporally-aware multimodal tokens to train a large, general-purpose model for egocentric 4D understanding. This unified design supports multitasking across diverse egocentric perception and synthesis tasks, including gaze prediction, egocentric camera tracking, and monocular depth estimation from egocentric video, and also serves as a generative model for conditional egocentric video synthesis. Across these tasks, EgoM2P matches or outperforms specialist models while being an order of magnitude faster. We will fully open-source EgoM2P to support the community and advance egocentric vision research.
|
2506.07886
|
Accepted by ICCV 2025
| null |
https://egom2p.github.io/
|
[] |
[] |
[] |
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] |
336
|
On Large Multimodal Models as Open-World Image Classifiers
|
[
"Alessandro Conti",
"Massimiliano Mancini",
"Enrico Fini",
"Yiming Wang",
"Paolo Rota",
"Elisa Ricci"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Conti_On_Large_Multimodal_Models_as_Open-World_Image_Classifiers_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Conti_On_Large_Multimodal_Models_as_Open-World_Image_Classifiers_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Conti_On_Large_Multimodal_ICCV_2025_supplemental.pdf
|
@InProceedings{Conti_2025_ICCV,
author = {Conti, Alessandro and Mancini, Massimiliano and Fini, Enrico and Wang, Yiming and Rota, Paolo and Ricci, Elisa},
title = {On Large Multimodal Models as Open-World Image Classifiers},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {16388-16398}
}
|
Traditional image classification requires a predefined list of semantic categories. In contrast, Large Multimodal Models (LMMs) can sidestep this requirement by classifying images directly using natural language (e.g., answering the prompt "What is the main object in the image?"). Despite this remarkable capability, most existing studies on LMM classification performance are surprisingly limited in scope, often assuming a closed-world setting with a predefined set of categories. In this work, we address this gap by thoroughly evaluating LMM classification performance in a truly open-world setting. We first formalize the task and introduce an evaluation protocol, defining various metrics to assess the alignment between predicted and ground truth classes. We then evaluate 13 models across 10 benchmarks, encompassing prototypical, non-prototypical, fine-grained, and very fine-grained classes, demonstrating the challenges LMMs face in this task. Further analyses based on the proposed metrics reveal the types of errors LMMs make, highlighting challenges related to granularity and fine-grained capabilities, showing how tailored prompting and reasoning can alleviate them.
|
2503.21851
|
23 pages, 13 figures, code is available at
https://github.com/altndrr/lmms-owc
| null | null |
[] |
[] |
[] |
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] |
337
|
Adaptive Hyper-Graph Convolution Network for Skeleton-based Human Action Recognition with Virtual Connections
|
[
"Youwei Zhou",
"Tianyang Xu",
"Cong Wu",
"Xiaojun Wu",
"Josef Kittler"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhou_Adaptive_Hyper-Graph_Convolution_Network_for_Skeleton-based_Human_Action_Recognition_with_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_Adaptive_Hyper-Graph_Convolution_Network_for_Skeleton-based_Human_Action_Recognition_with_ICCV_2025_paper.pdf
| null |
@InProceedings{Zhou_2025_ICCV,
author = {Zhou, Youwei and Xu, Tianyang and Wu, Cong and Wu, Xiaojun and Kittler, Josef},
title = {Adaptive Hyper-Graph Convolution Network for Skeleton-based Human Action Recognition with Virtual Connections},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {12648-12658}
}
|
The shared topology of human skeletons motivated the recent investigation of graph convolutional network (GCN) solutions for action recognition.However, most of the existing GCNs rely on the binary connection of two neighboring vertices (joints) formed by an edge (bone), overlooking the potential of constructing multi-vertex convolution structures.Although some studies have attempted to utilize hyper-graphs to represent the topology, they rely on a fixed construction strategy, which limits their adaptivity in uncovering the intricate latent relationships within the action.In this paper, we address this oversight and explore the merits of an adaptive hyper-graph convolutional network (Hyper-GCN) to achieve the aggregation of rich semantic information conveyed by skeleton vertices.In particular, our Hyper-GCN adaptively optimises the hyper-graphs during training, revealing the action-driven multi-vertex relations. Besides, virtual connections are often designed to support efficient feature aggregation, implicitly extending the spectrum of dependencies within the skeleton.By injecting virtual connections into hyper-graphs, the semantic clues of diverse action categories can be highlighted. The results of experiments conducted on the NTU-60, NTU-120, and NW-UCLA datasets demonstrate the merits of our Hyper-GCN, compared to the state-of-the-art methods. The code is available at https://github.com/6UOOON9/Hyper-GCN.
|
2411.14796
| null |
https://github.com/6UOOON9/Hyper-GCN
| null |
[] |
[] |
[] |
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] |
338
|
Simultaneous Motion And Noise Estimation with Event Cameras
|
[
"Shintaro Shiba",
"Yoshimitsu Aoki",
"Guillermo Gallego"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Shiba_Simultaneous_Motion_And_Noise_Estimation_with_Event_Cameras_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Shiba_Simultaneous_Motion_And_Noise_Estimation_with_Event_Cameras_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Shiba_Simultaneous_Motion_And_ICCV_2025_supplemental.pdf
|
@InProceedings{Shiba_2025_ICCV,
author = {Shiba, Shintaro and Aoki, Yoshimitsu and Gallego, Guillermo},
title = {Simultaneous Motion And Noise Estimation with Event Cameras},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {6959-6969}
}
|
Event cameras are emerging vision sensors whose noise is challenging to characterize. Existing denoising methods for event cameras are often designed in isolation and thus consider other tasks, such as motion estimation, separately (i.e., sequentially after denoising). However, motion is an intrinsic part of event data, since scene edges cannot be sensed without motion. We propose, to the best of our knowledge, the first method that simultaneously estimates motion in its various forms (e.g., ego-motion, optical flow) and noise. The method is flexible, as it allows replacing the one-step motion estimation of the widely-used Contrast Maximization framework with any other motion estimator, such as deep neural networks. The experiments show that the proposed method achieves state-of-the-art results on the E-MLB denoising benchmark and competitive results on the DND21 benchmark, while demonstrating effectiveness across motion estimation and intensity reconstruction tasks. Our approach advances event-data denoising theory and expands practical denoising use-cases via open-source code. Project page: https://github.com/tub-rip/ESMD
|
2504.04029
|
13 pages, 13 figures, 6 tables, Project page
https://github.com/tub-rip/ESMD
|
https://github.com/tub-rip/ESMD
| null |
[] |
[] |
[] |
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] |
339
|
Supercharging Floorplan Localization with Semantic Rays
|
[
"Yuval Grader",
"Hadar Averbuch-Elor"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Grader_Supercharging_Floorplan_Localization_with_Semantic_Rays_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Grader_Supercharging_Floorplan_Localization_with_Semantic_Rays_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Grader_Supercharging_Floorplan_Localization_ICCV_2025_supplemental.pdf
|
@InProceedings{Grader_2025_ICCV,
author = {Grader, Yuval and Averbuch-Elor, Hadar},
title = {Supercharging Floorplan Localization with Semantic Rays},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27116-27125}
}
|
Floorplans provide a compact representation of the building's structure, revealing not only layout information but also detailed semantics such as the locations of windows and doors. However, contemporary floorplan localization techniques mostly focus on matching depth-based structural cues, ignoring the rich semantics communicated within floorplans. In this work, we introduce a semantic-aware localization framework that jointly estimates depth and semantic rays, consolidating over both for predicting a structural-semantic probability volume. Our probability volume is constructed in a coarse-to-fine manner: We first sample a small set of rays to obtain an initial low-resolution probability volume. We then refine these probabilities by performing a denser sampling only in high-probability regions and process the refined values for predicting a 2D location and orientation angle. We conduct an evaluation on two standard floorplan localization benchmarks. Our experiments demonstrate that our approach substantially outperforms state-of-the-art methods, achieving significant improvements in recall metrics compared to prior works. Moreover, we show that our framework can easily incorporate additional metadata such as room labels, enabling additional gains in both accuracy and efficiency.
|
2507.09291
|
Accepted at ICCV 2025. https://tau-vailab.github.io/SemRayLoc/
| null | null |
[] |
[] |
[] |
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] |
340
|
MDP3: A Training-free Approach for List-wise Frame Selection in Video-LLMs
|
[
"Hui Sun",
"Shiyin Lu",
"Huanyu Wang",
"Qing-Guo Chen",
"Zhao Xu",
"Weihua Luo",
"Kaifu Zhang",
"Ming Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Sun_MDP3_A_Training-free_Approach_for_List-wise_Frame_Selection_in_Video-LLMs_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Sun_MDP3_A_Training-free_Approach_for_List-wise_Frame_Selection_in_Video-LLMs_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Sun_MDP3_A_Training-free_ICCV_2025_supplemental.pdf
|
@InProceedings{Sun_2025_ICCV,
author = {Sun, Hui and Lu, Shiyin and Wang, Huanyu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu and Li, Ming},
title = {MDP3: A Training-free Approach for List-wise Frame Selection in Video-LLMs},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24090-24101}
}
|
Video large language models (Video-LLMs) have made significant progress in understanding videos. However, processing multiple frames leads to lengthy visual token sequences, presenting challenges such as the limited context length cannot accommodate the entire video, and the inclusion of irrelevant frames hinders visual perception. Hence, effective frame selection is crucial. This paper emphasizes that frame selection should follow three key principles: query relevance, list-wise diversity, and sequentiality. Existing methods, such as uniform frame sampling and query-frame matching, do not capture all of these principles. Thus, we propose Markov decision determinantal point process with dynamic programming (MDP^3) for frame selection, a training-free and model-agnostic method that can be seamlessly integrated into existing Video-LLMs. Our method first estimates frame similarities conditioned on the query using a conditional Gaussian kernel within the reproducing kernel Hilbert space (RKHS). We then apply the determinantal point process (DPP) to the similarity matrix to capture both query relevance and list-wise diversity. To incorporate sequentiality, we segment the video and apply DPP within each segment, conditioned on the preceding segment selection, modeled as a Markov decision process (MDP) for allocating selection sizes across segments. Theoretically, MDP^3 provides a (1-1/e)-approximate solution to the NP-hard list-wise frame selection problem with pseudo-polynomial time complexity, demonstrating its efficiency. Empirically, MDP significantly outperforms existing methods, verifying its effectiveness and robustness.
|
2501.02885
|
24 pages, 10 figures
| null | null |
[] |
[] |
[] |
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] |
341
|
General Compression Framework for Efficient Transformer Object Tracking
|
[
"Lingyi Hong",
"Jinglun Li",
"Xinyu Zhou",
"Shilin Yan",
"Pinxue Guo",
"Kaixun Jiang",
"Zhaoyu Chen",
"Shuyong Gao",
"Runze Li",
"Xingdong Sheng",
"Wei Zhang",
"Hong Lu",
"Wenqiang Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Hong_General_Compression_Framework_for_Efficient_Transformer_Object_Tracking_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Hong_General_Compression_Framework_for_Efficient_Transformer_Object_Tracking_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Hong_General_Compression_Framework_ICCV_2025_supplemental.pdf
|
@InProceedings{Hong_2025_ICCV,
author = {Hong, Lingyi and Li, Jinglun and Zhou, Xinyu and Yan, Shilin and Guo, Pinxue and Jiang, Kaixun and Chen, Zhaoyu and Gao, Shuyong and Li, Runze and Sheng, Xingdong and Zhang, Wei and Lu, Hong and Zhang, Wenqiang},
title = {General Compression Framework for Efficient Transformer Object Tracking},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13427-13437}
}
|
Previous works have attempted to improve tracking efficiency through lightweight architecture design or knowledge distillation from teacher models to compact student trackers. However, these solutions often sacrifice accuracy for speed to a great extent, and also have the problems of complex training process and structural limitations. Thus, we propose a general model compression framework for efficient transformer object tracking, named CompressTracker, to reduce model size while preserving tracking accuracy. Our approach features a novel stage division strategy that segments the transformer layers of the teacher model into distinct stages to break the limitation of model structure. Additionally, we also design a unique replacement training technique that randomly substitutes specific stages in the student model with those from the teacher model, as opposed to training the student model in isolation. Replacement training enhances the student model's ability to replicate the teacher model's behavior and simplifies the training process. To further forcing student model to emulate teacher model, we incorporate prediction guidance and stage-wise feature mimicking to provide additional supervision during the teacher model's compression process. CompressTracker is structurally agnostic, making it compatible with any transformer architecture. We conduct a series of experiment to verify the effectiveness and generalizability of our CompressTracker. Our CompressTracker-SUTrack, compressed from SUTrack, retains about 99% performance on LaSOT (72.2% AUC) while achieves 2.42x speed up. Code is available at https://github.com/LingyiHongfd/CompressTracker.
|
2409.17564
|
Accepted to ICCV 2025
|
https://github.com/LingyiHongfd/CompressTracker
| null |
[] |
[] |
[] |
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] |
342
|
Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts
|
[
"Chiao-An Yang",
"Kuan-Chuan Peng",
"Raymond A. Yeh"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yang_Toward_Long-Tailed_Online_Anomaly_Detection_through_Class-Agnostic_Concepts_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yang_Toward_Long-Tailed_Online_Anomaly_Detection_through_Class-Agnostic_Concepts_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yang_Toward_Long-Tailed_Online_ICCV_2025_supplemental.pdf
|
@InProceedings{Yang_2025_ICCV,
author = {Yang, Chiao-An and Peng, Kuan-Chuan and Yeh, Raymond A.},
title = {Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {23419-23430}
}
|
Anomaly detection (AD) identifies the defect regions of a given image. Recent works have studied AD, focusing on learning AD without abnormal images, with long-tailed distributed training data, and using a unified model for all classes. In addition, online AD learning has also been explored. In this work, we expand in both directions to a realistic setting by considering the new novel task of long-tailed online AD (LTOAD). We first identified that the offline state-of-the-art LTAD methods cannot be directly applied to the online setting. Specifically, LTAD is class-aware, requiring class labels that are not available in the online setting. To address this challenge, we propose a class-agnostic framework for LTAD and then adapt it to our online learning setting. Our method outperforms the SOTA baselines in most offline LTAD settings, including both the industrial manufacturing and the medical domain. In particular, we observe +4.63% image-AUROC on MVTec even compared to methods that have access to class labels and the number of classes. In the most challenging long-tailed online setting, we achieve +0.53% image-AUROC compared to baselines.
|
2507.16946
|
This paper is accepted to ICCV 2025. The supplementary material is
included. The long-tailed online anomaly detection dataset is available at
https://doi.org/10.5281/zenodo.16283852
| null |
https://arxiv.org/abs/2507.16946
|
[] |
[] |
[] |
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] |
343
|
CIARD: Cyclic Iterative Adversarial Robustness Distillation
|
[
"Liming Lu",
"Shuchao Pang",
"Xu Zheng",
"Xiang Gu",
"Anan Du",
"Yunhuai Liu",
"Yongbin Zhou"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Lu_CIARD_Cyclic_Iterative_Adversarial_Robustness_Distillation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Lu_CIARD_Cyclic_Iterative_Adversarial_Robustness_Distillation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Lu_CIARD_Cyclic_Iterative_ICCV_2025_supplemental.pdf
|
@InProceedings{Lu_2025_ICCV,
author = {Lu, Liming and Pang, Shuchao and Zheng, Xu and Gu, Xiang and Du, Anan and Liu, Yunhuai and Zhou, Yongbin},
title = {CIARD: Cyclic Iterative Adversarial Robustness Distillation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {350-359}
}
|
Adversarial robustness distillation (ARD) aims to transfer both performance and robustness from teacher model to lightweight student model, enabling resilient performance on resource-constrained scenarios. Though existing ARD approaches enhance student model's robustness, the inevitable by-product leads to the degraded performance on clean examples. We summarize the causes of this problem inherent in existing methods with dual-teacher framework as: 1 The divergent optimization objectives of dual-teacher models, i.e., the clean and robust teachers, impede effective knowledge transfer to the student model, and 2 The iteratively generated adversarial examples during training lead to performance deterioration of the robust teacher model. To address these challenges, we propose a novel Cyclic Iterative ARD (CIARD) method with two key innovations: 1 A multi-teacher framework with contrastive push-loss alignment to resolve conflicts in dual-teacher optimization objectives, and 2 Continuous adversarial retraining to maintain dynamic teacher robustness against performance degradation from the varying adversarial examples. Extensive experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate that CIARD achieves remarkable performance with an average 3.53% improvement in adversarial defense rates across various attack scenarios and a 5.87% increase in clean sample accuracy, establishing a new benchmark for balancing model robustness and generalization. Our code is available at https://github.com/CIARD2025/CIARD.
|
2509.12633
| null | null | null |
[] |
[] |
[] |
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] |
344
|
DiffTell: A High-Quality Dataset for Describing Image Manipulation Changes
|
[
"Zonglin Di",
"Jing Shi",
"Yifei Fan",
"Hao Tan",
"Alexander Black",
"John Collomosse",
"Yang Liu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Di_DiffTell_A_High-Quality_Dataset_for_Describing_Image_Manipulation_Changes_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Di_DiffTell_A_High-Quality_Dataset_for_Describing_Image_Manipulation_Changes_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Di_DiffTell_A_High-Quality_ICCV_2025_supplemental.pdf
|
@InProceedings{Di_2025_ICCV,
author = {Di, Zonglin and Shi, Jing and Fan, Yifei and Tan, Hao and Black, Alexander and Collomosse, John and Liu, Yang},
title = {DiffTell: A High-Quality Dataset for Describing Image Manipulation Changes},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24580-24590}
}
|
The image difference captioning (IDC) task is to describe the distinctions between two images. However, existing datasets do not offer comprehensive coverage across all image-difference categories. In this work, we introduce a high-quality dataset, DiffTell with various types of image manipulations, including global image alterations, object-level changes, and text manipulations. The data quality is controlled by careful human filtering. Additionally, to scale up the data collection without prohibitive human labor costs, we explore the possibility of automatically filtering for quality control. We demonstrate that both traditional methods and recent multimodal large language models (MLLMs) exhibit performance improvements on the IDC task after training on the DiffTell dataset. Through extensive ablation studies, we provide a detailed analysis of the performance gains attributed to DiffTell. Experiments show DiffTell significantly enhances the availability of resources for IDC research, offering a more comprehensive foundation and benchmark for future investigations.
| null | null | null | null |
[] |
[] |
[] |
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] |
345
|
A Plug-and-Play Physical Motion Restoration Approach for In-the-Wild High-Difficulty Motions
|
[
"Youliang Zhang",
"Ronghui Li",
"Yachao Zhang",
"Liang Pan",
"Jingbo Wang",
"Yebin Liu",
"Xiu Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_A_Plug-and-Play_Physical_Motion_Restoration_Approach_for_In-the-Wild_High-Difficulty_Motions_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_A_Plug-and-Play_Physical_Motion_Restoration_Approach_for_In-the-Wild_High-Difficulty_Motions_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_A_Plug-and-Play_Physical_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Youliang and Li, Ronghui and Zhang, Yachao and Pan, Liang and Wang, Jingbo and Liu, Yebin and Li, Xiu},
title = {A Plug-and-Play Physical Motion Restoration Approach for In-the-Wild High-Difficulty Motions},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13281-13292}
}
|
Extracting physically plausible 3D human motion from videos is a critical task. Although existing simulation-based motion imitation methods can enhance the physical quality of daily motions estimated from monocular video capture, extending this capability to high-difficulty motions remains an open challenge. This can be attributed to some flawed motion clips in video-based motion capture results and the inherent complexity in modeling high-difficulty motions. Therefore, sensing the advantage of segmentation in localizing human body, we introduce a mask-based motion correction module (MCM) that leverages motion context and video mask to repair flawed motions; and propose a physics-based motion transfer module (PTM), which employs a prior injected pretrain and adapt approach for motion imitation, improving physical plausibility with the ability to handle in-the-wild and challenging motions. Our approach is designed as a plug-and-play module to physically refine the video motion capture, which also excels in motion generation tasks. Finally, we collected a challenging in-the-wild test set to establish a benchmark, and our method has demonstrated effectiveness on both the new benchmark and existing public datasets. Our project page is : https://physicalmotionrestoration.github.io/
|
2412.17377
| null | null | null |
[] |
[] |
[] |
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] |
346
|
Local Dense Logit Relations for Enhanced Knowledge Distillation
|
[
"Liuchi Xu",
"Kang Liu",
"Jinshuai Liu",
"Lu Wang",
"Lisheng Xu",
"Jun Cheng"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xu_Local_Dense_Logit_Relations_for_Enhanced_Knowledge_Distillation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xu_Local_Dense_Logit_Relations_for_Enhanced_Knowledge_Distillation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xu_Local_Dense_Logit_ICCV_2025_supplemental.pdf
|
@InProceedings{Xu_2025_ICCV,
author = {Xu, Liuchi and Liu, Kang and Liu, Jinshuai and Wang, Lu and Xu, Lisheng and Cheng, Jun},
title = {Local Dense Logit Relations for Enhanced Knowledge Distillation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4539-4549}
}
|
State-of-the-art logit distillation methods exhibit versatility, simplicity, and efficiency.Despite the advances, existing studies have yet to delve thoroughly into fine-grained relationships within logit knowledge.In this paper, we propose Local Dense Relational Logit Distillation (LDRLD), a novel method that captures inter-class relationships through recursively decoupling and recombining logit information, thereby providing more detailed and clearer insights for student learning.To further optimize the performance, we introduce an Adaptive Decay Weight (ADW) strategy, which can dynamically adjust the weights for critical category pairs using Inverse Rank Weighting (IRW) and Exponential Rank Decay (ERD).Specifically, IRW assigns weights inversely proportional to the rank differences between pairs, while ERD adaptively controls weight decay based on total ranking scores of category pairs. Furthermore, after the recursive decoupling, we distill the remaining non-target knowledge to ensure knowledge completeness and enhance performance. Ultimately, our method improves the student's performance by transferring fine-grained knowledge and emphasizing the most critical relationships.Extensive experiments on datasets such as CIFAR-100, ImageNet-1K, and Tiny-ImageNet demonstrate that our method compares favorably with state-of-the-art logit-based knowledge distillation approaches. The code will be made publicly available.
|
2507.15911
|
Accepted by ICCV2025
| null | null |
[] |
[] |
[] |
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] |
347
|
Less is More: Empowering GUI Agent with Context-Aware Simplification
|
[
"Gongwei Chen",
"Xurui Zhou",
"Rui Shao",
"Yibo Lyu",
"Kaiwen Zhou",
"Shuai Wang",
"Wentao Li",
"Yinchuan Li",
"Zhongang Qi",
"Liqiang Nie"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_Less_is_More_Empowering_GUI_Agent_with_Context-Aware_Simplification_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_Less_is_More_Empowering_GUI_Agent_with_Context-Aware_Simplification_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_Less_is_More_ICCV_2025_supplemental.pdf
|
@InProceedings{Chen_2025_ICCV,
author = {Chen, Gongwei and Zhou, Xurui and Shao, Rui and Lyu, Yibo and Zhou, Kaiwen and Wang, Shuai and Li, Wentao and Li, Yinchuan and Qi, Zhongang and Nie, Liqiang},
title = {Less is More: Empowering GUI Agent with Context-Aware Simplification},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {5901-5911}
}
|
The research focus of GUI agents is shifting from text-dependent to pure-vision-based approaches, which, though promising, prioritize comprehensive pre-training data collection while neglecting contextual modeling challenges. We probe the characteristics of element and history contextual modeling in GUI agents and summarize: **1) the high-density and loose-relation of element context** highlight the existence of many unrelated elements and their negative influence; **2) the high redundancy of history context** reveals the inefficient history modeling in current GUI agents. In this work, we propose a context-aware simplification framework for building an efficient and effective GUI Agent, termed **SimpAgent**. To mitigate potential interference from numerous unrelated elements, we introduce a **masking-based element pruning** method that circumvents the intractable relation modeling through an efficient masking mechanism. To reduce the redundancy in historical information, we devise a **consistency-guided history compression** module, which enhances implicit LLM-based compression through innovative explicit guidance, achieving an optimal balance between performance and efficiency. With the above components, SimpAgent reduces 27% FLOPs and achieves superior GUI navigation performances. Comprehensive navigation experiments across diverse web and mobile environments demonstrate the effectiveness and potential of our agent.
|
2507.03730
|
Accepted to ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
348
|
TrafficLoc: Localizing Traffic Surveillance Cameras in 3D Scenes
|
[
"Yan Xia",
"Yunxiang Lu",
"Rui Song",
"Oussema Dhaouadi",
"João F. Henriques",
"Daniel Cremers"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xia_TrafficLoc_Localizing_Traffic_Surveillance_Cameras_in_3D_Scenes_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xia_TrafficLoc_Localizing_Traffic_Surveillance_Cameras_in_3D_Scenes_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xia_TrafficLoc_Localizing_Traffic_ICCV_2025_supplemental.pdf
|
@InProceedings{Xia_2025_ICCV,
author = {Xia, Yan and Lu, Yunxiang and Song, Rui and Dhaouadi, Oussema and Henriques, Jo\~ao F. and Cremers, Daniel},
title = {TrafficLoc: Localizing Traffic Surveillance Cameras in 3D Scenes},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {28685-28695}
}
|
We tackle the problem of localizing traffic cameras within a 3D reference map and propose a novel image-to-point cloud registration (I2P) method, TrafficLoc, in a coarse-to-fine matching fashion. To overcome the lack of large-scale real-world intersection datasets, we first introduce Carla Intersection, a new simulated dataset with 75 urban and rural intersections in Carla. We find that current I2P methods struggle with cross-modal matching under large viewpoint differences, especially at traffic intersections. TrafficLoc thus employs a novel Geometry-guided Attention Loss (GAL) to focus only on the corresponding geometric regions under different viewpoints during 2D-3D feature fusion. To address feature inconsistency in paired image patch-point groups, we further propose Inter-intra Contrastive Learning (ICL) to enhance separating 2D patch / 3D group features within each intra-modality and introduce Dense Training Alignment (DTA) with soft-argmax for improving position regression. Extensive experiments show our TrafficLoc greatly improves the performance over the SOTA I2P methods (up to 86%) on Carla Intersection and generalizes well to real-world data. TrafficLoc also achieves new SOTA performance on KITTI and NuScenes datasets, demonstrating the superiority across both in-vehicle and traffic cameras. Our project page is publicly available at https://tum-luk.github.io/projects/trafficloc/.
|
2412.10308
| null | null | null |
[] |
[] |
[] |
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] |
349
|
Detect Anything 3D in the Wild
|
[
"Hanxue Zhang",
"Haoran Jiang",
"Qingsong Yao",
"Yanan Sun",
"Renrui Zhang",
"Hao Zhao",
"Hongyang Li",
"Hongzi Zhu",
"Zetong Yang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_Detect_Anything_3D_in_the_Wild_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_Detect_Anything_3D_in_the_Wild_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_Detect_Anything_3D_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Hanxue and Jiang, Haoran and Yao, Qingsong and Sun, Yanan and Zhang, Renrui and Zhao, Hao and Li, Hongyang and Zhu, Hongzi and Yang, Zetong},
title = {Detect Anything 3D in the Wild},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {5048-5059}
}
|
Despite the success of deep learning in close-set 3D object detection, existing approaches struggle with zero-shot generalization to novel objects and camera configurations. We introduce DetAny3D, a promptable 3D detection foundation model capable of detecting any novel object under arbitrary camera configurations using only monocular inputs. Training a foundation model for 3D detection is fundamentally constrained by the limited availability of annotated 3D data, which motivates DetAny3D to leverage the rich prior knowledge embedded in extensively pre-trained 2D foundation models to compensate for this scarcity. To effectively transfer 2D knowledge to 3D, DetAny3D incorporates two core modules: the 2D Aggregator, which aligns features from different 2D foundation models, and the 3D Interpreter with Zero-Embedding Mapping, which stabilizes early training in 2D-to-3D knowledge transfer. Experimental results validate the strong generalization of our DetAny3D, which not only achieves state-of-the-art performance on unseen categories and novel camera configurations, but also surpasses most competitors on in-domain data. DetAny3D sheds light on the potential of the 3D foundation model for diverse applications in real-world scenarios, e.g., rare object detection in autonomous driving, and demonstrates promise for further exploration of 3D-centric tasks in open-world settings. More visualization results can be found at our code repository.
|
2504.07958
| null | null | null |
[] |
[] |
[] |
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] |
350
|
Failure Cases Are Better Learned But Boundary Says Sorry: Facilitating Smooth Perception Change for Accuracy-Robustness Trade-Off in Adversarial Training
|
[
"Yanyun Wang",
"Li Liu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Failure_Cases_Are_Better_Learned_But_Boundary_Says_Sorry_Facilitating_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Failure_Cases_Are_Better_Learned_But_Boundary_Says_Sorry_Facilitating_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_Failure_Cases_Are_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Yanyun and Liu, Li},
title = {Failure Cases Are Better Learned But Boundary Says Sorry: Facilitating Smooth Perception Change for Accuracy-Robustness Trade-Off in Adversarial Training},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4691-4700}
}
|
Adversarial Training (AT) is one of the most effective methods to train robust Deep Neural Networks (DNNs). However, AT creates an inherent trade-off between clean accuracy and adversarial robustness, which is commonly attributed to the more complicated decision boundary caused by the insufficient learning of hard adversarial samples. In this work, we reveal a counterintuitive fact for the first time: **From the perspective of perception consistency, hard adversarial samples that can still attack the robust model after AT are already learned better than those successfully defended**. Thus, different from previous views, we argue that it is rather the over-sufficient learning of hard adversarial samples that degrades the decision boundary and contributes to the trade-off problem. Specifically, the excessive pursuit of perception consistency would force the model to view the perturbations as noise and ignore the information within them, which should have been utilized to induce a smoother perception transition towards the decision boundary to support its establishment to an appropriate location. In response, we define a new AT objective named **Robust Perception**, encouraging the model perception to change smoothly with input perturbations, based on which we propose a novel **R**obust **P**erception **A**dversarial **T**raining (**RPAT**) method, effectively mitigating the current accuracy-robustness trade-off. Experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet with ResNet-18, PreActResNet-18, and WideResNet-34-10 demonstrate the effectiveness of our method beyond four common baselines and 12 state-of-the-art (SOTA) works. The code is available at https://github.com/FlaAI/RPAT.
|
2508.02186
|
2025 IEEE/CVF International Conference on Computer Vision (ICCV'25)
|
https://github.com/FlaAI/RPAT
| null |
[] |
[] |
[] |
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] |
351
|
CA-I2P: Channel-Adaptive Registration Network with Global Optimal Selection
|
[
"Zhixin Cheng",
"Jiacheng Deng",
"Xinjun Li",
"Xiaotian Yin",
"Bohao Liao",
"Baoqun Yin",
"Wenfei Yang",
"Tianzhu Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Cheng_CA-I2P_Channel-Adaptive_Registration_Network_with_Global_Optimal_Selection_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Cheng_CA-I2P_Channel-Adaptive_Registration_Network_with_Global_Optimal_Selection_ICCV_2025_paper.pdf
| null |
@InProceedings{Cheng_2025_ICCV,
author = {Cheng, Zhixin and Deng, Jiacheng and Li, Xinjun and Yin, Xiaotian and Liao, Bohao and Yin, Baoqun and Yang, Wenfei and Zhang, Tianzhu},
title = {CA-I2P: Channel-Adaptive Registration Network with Global Optimal Selection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27739-27749}
}
|
Detection-free methods typically follow a coarse-to-fine pipeline, extracting image and point cloud features for patch-level matching and refining dense pixel-to-point correspondences. However, differences in feature channel attention between images and point clouds may lead to degraded matching results, ultimately impairing registration accuracy.Furthermore, similar structures in the scene could lead to redundant correspondences in cross-modal matching.To address these issues, we propose Channel Adaptive Adjustment Module (CAA) and Global Optimal Selection Module (GOS). CAA enhances intra-modal features and suppresses cross-modal sensitivity, while GOS replaces local selection with global optimization. Experiments on RGB-D Scenes V2 and 7-Scenes demonstrate the superiority of our method, achieving state-of-the-art performance in image-to-point cloud registration.
| null | null | null | null |
[] |
[] |
[] |
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] |
352
|
Exploiting Vision Language Model for Training-Free 3D Point Cloud OOD Detection via Graph Score Propagation
|
[
"Tiankai Chen",
"Yushu Li",
"Adam Goodge",
"Fei Teng",
"Xulei Yang",
"Tianrui Li",
"Xun Xu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_Exploiting_Vision_Language_Model_for_Training-Free_3D_Point_Cloud_OOD_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_Exploiting_Vision_Language_Model_for_Training-Free_3D_Point_Cloud_OOD_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_Exploiting_Vision_Language_ICCV_2025_supplemental.pdf
|
@InProceedings{Chen_2025_ICCV,
author = {Chen, Tiankai and Li, Yushu and Goodge, Adam and Teng, Fei and Yang, Xulei and Li, Tianrui and Xu, Xun},
title = {Exploiting Vision Language Model for Training-Free 3D Point Cloud OOD Detection via Graph Score Propagation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {28797-28807}
}
|
Out-of-distribution (OOD) detection in 3D point cloud data remains a challenge, particularly in applications where safe and robust perception is critical. While existing OOD detection methods have shown progress for 2D image data, extending these to 3D environments involves unique obstacles. This paper introduces a training-free framework that leverages Vision-Language Models (VLMs) for effective OOD detection in 3D point clouds. By constructing a graph based on class prototypes and testing data, we exploit the data manifold structure to enhancing the effectiveness of VLMs for 3D OOD detection. We propose a novel Graph Score Propagation (GSP) method that incorporates prompt clustering and self-training negative prompting to improve OOD scoring with VLM. Our method is also adaptable to few-shot scenarios, providing options for practical applications. We demonstrate that GSP consistently outperforms state-of-the-art methods across synthetic and real-world datasets for 3D point cloud OOD detection.
|
2506.22375
|
Accepted by ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
353
|
Monocular Facial Appearance Capture in the Wild
|
[
"Yingyan Xu",
"Kate Gadola",
"Prashanth Chandran",
"Sebastian Weiss",
"Markus Gross",
"Gaspard Zoss",
"Derek Bradley"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xu_Monocular_Facial_Appearance_Capture_in_the_Wild_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xu_Monocular_Facial_Appearance_Capture_in_the_Wild_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xu_Monocular_Facial_Appearance_ICCV_2025_supplemental.pdf
|
@InProceedings{Xu_2025_ICCV,
author = {Xu, Yingyan and Gadola, Kate and Chandran, Prashanth and Weiss, Sebastian and Gross, Markus and Zoss, Gaspard and Bradley, Derek},
title = {Monocular Facial Appearance Capture in the Wild},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {12078-12088}
}
|
We present a new method for reconstructing the appearance properties of human faces from a lightweight capture procedure in an unconstrained environment. Our method recovers the surface geometry, diffuse albedo, specular intensity and specular roughness from a monocular video containing a simple head rotation in-the-wild. Notably, we make no simplifying assumptions on the environment lighting, and we explicitly take visibility and occlusions into account. As a result, our method can produce facial appearance maps that approach the fidelity of studio-based multi-view captures, but with a far easier and cheaper procedure.
|
2412.12765
| null | null | null |
[] |
[] |
[] |
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] |
354
|
Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis
|
[
"Bowen Zhang",
"Sicheng Xu",
"Chuxin Wang",
"Jiaolong Yang",
"Feng Zhao",
"Dong Chen",
"Baining Guo"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_Gaussian_Variation_Field_Diffusion_for_High-fidelity_Video-to-4D_Synthesis_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_Gaussian_Variation_Field_Diffusion_for_High-fidelity_Video-to-4D_Synthesis_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_Gaussian_Variation_Field_ICCV_2025_supplemental.zip
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Bowen and Xu, Sicheng and Wang, Chuxin and Yang, Jiaolong and Zhao, Feng and Chen, Dong and Guo, Baining},
title = {Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {12502-12513}
}
|
In this paper, we present a novel framework for video-to-4D generation that creates high-quality dynamic 3D content from single video inputs. Direct 4D diffusion modeling is extremely challenging due to costly data construction and the high-dimensional nature of jointly representing 3D shape, appearance, and motion. We address these challenges by introducing a Direct 4DMesh-to-GS Variation Field VAE that directly encodes canonical Gaussian Splats (GS) and their temporal variations from 3D animation data without per-instance fitting, and compresses high-dimensional animations into a compact latent space. Building upon this efficient representation, we train a Gaussian Variation Field diffusion model with temporal-aware Diffusion Transformer conditioned on input videos and canonical GS. Trained on carefully-curated animatable 3D objects from the Objaverse dataset, our model demonstrates superior generation quality compared to existing methods. It also exhibits remarkable generalization to in-the-wild video inputs despite being trained exclusively on synthetic data, paving the way for generating high-quality animated 3D content. Project page: https://gvfdiffusion.github.io/.
|
2507.23785
|
ICCV 2025. Project page: https://gvfdiffusion.github.io/
| null | null |
[] |
[
"BwZhang/GaussianVariationFieldDiffusion"
] |
[] |
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] |
355
|
Can3Tok: Canonical 3D Tokenization and Latent Modeling of Scene-Level 3D Gaussians
|
[
"Quankai Gao",
"Iliyan Georgiev",
"Tuanfeng Y. Wang",
"Krishna Kumar Singh",
"Ulrich Neumann",
"Jae Shin Yoon"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Gao_Can3Tok_Canonical_3D_Tokenization_and_Latent_Modeling_of_Scene-Level_3D_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Gao_Can3Tok_Canonical_3D_Tokenization_and_Latent_Modeling_of_Scene-Level_3D_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Gao_Can3Tok_Canonical_3D_ICCV_2025_supplemental.pdf
|
@InProceedings{Gao_2025_ICCV,
author = {Gao, Quankai and Georgiev, Iliyan and Wang, Tuanfeng Y. and Singh, Krishna Kumar and Neumann, Ulrich and Yoon, Jae Shin},
title = {Can3Tok: Canonical 3D Tokenization and Latent Modeling of Scene-Level 3D Gaussians},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9320-9331}
}
|
3D generation has made significant progress, however, it still largely remains at the object-level. Feedforward 3D scene-level generation has been rarely explored due to the lack of models capable of scaling-up latent representation learning on 3D scene-level data. Unlike object-level generative models, which are trained on well-labeled 3D data in a bounded canonical space, scene-level generations with 3D scenes represented by 3D Gaussian Splatting (3DGS) are unbounded and exhibit scale inconsistency across different scenes, making unified latent representation learning for generative purposes extremely challenging. In this paper, we introduce Can3Tok, the first 3D scene-level variational autoencoder (VAE) capable of encoding a large number of Gaussian primitives into a low-dimensional latent embedding, which effectively captures both semantic and spatial information of the inputs. Beyond model design, we propose a general pipeline for 3D scene data processing to address scale inconsistency issue. We validate our method on the recent scene-level 3D dataset DL3DV-10K, where we found that only Can3Tok successfully generalizes to novel 3D scenes, while compared methods fail to converge on even a few hundred scene inputs during training and exhibit zero generalization ability during inference. Finally, we demonstrate image-to-3DGS and text-to-3DGS generation as our applications to demonstrate it's ability to faciliate downstream generation tasks. Code will be released.
|
2508.01464
| null | null | null |
[] |
[] |
[] |
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356
|
EmotiCrafter: Text-to-Emotional-Image Generation based on Valence-Arousal Model
|
[
"Shengqi Dang",
"Yi He",
"Long Ling",
"Ziqing Qian",
"Nanxuan Zhao",
"Nan Cao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Dang_EmotiCrafter_Text-to-Emotional-Image_Generation_based_on_Valence-Arousal_Model_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Dang_EmotiCrafter_Text-to-Emotional-Image_Generation_based_on_Valence-Arousal_Model_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Dang_EmotiCrafter_Text-to-Emotional-Image_Generation_ICCV_2025_supplemental.pdf
|
@InProceedings{Dang_2025_ICCV,
author = {Dang, Shengqi and He, Yi and Ling, Long and Qian, Ziqing and Zhao, Nanxuan and Cao, Nan},
title = {EmotiCrafter: Text-to-Emotional-Image Generation based on Valence-Arousal Model},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {15218-15228}
}
|
Recent research shows that emotions can enhance users' cognition and influence information communication. While research on visual emotion analysis is extensive, limited work has been done on helping users generate emotionally rich image content. Existing work on emotional image generation relies on discrete emotion categories, making it challenging to capture complex and subtle emotional nuances accurately. Additionally, these methods struggle to control the specific content of generated images based on text prompts. In this paper, we introduce the task of continuous emotional image content generation (C-EICG) and present EmotiCrafter, a general emotional image generation model that generates images based on free text prompts and Valence-Arousal (V-A) values. It leverages a novel emotion-embedding mapping network to fuse V-A values into textual features, enabling the capture of emotions in alignment with intended input prompts. A novel loss function is also proposed to enhance emotion expression. The experimental results show that our method effectively generates images representing specific emotions with the desired content and outperforms existing techniques.
|
2501.05710
|
11 pages, 9 figures
| null | null |
[
"idvxlab/EmotiCrafter-Demo"
] |
[] |
[] |
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] |
357
|
HFD-Teacher: High-Frequency Depth Distillation from Depth Foundation Models for Enhanced Depth Completion
|
[
"Zhiyuan Yang",
"Anqi Cheng",
"Haiyue Zhu",
"Tianjiao Li",
"Pey Yuen Tao",
"Kezhi Mao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yang_HFD-Teacher_High-Frequency_Depth_Distillation_from_Depth_Foundation_Models_for_Enhanced_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yang_HFD-Teacher_High-Frequency_Depth_Distillation_from_Depth_Foundation_Models_for_Enhanced_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yang_HFD-Teacher_High-Frequency_Depth_ICCV_2025_supplemental.pdf
|
@InProceedings{Yang_2025_ICCV,
author = {Yang, Zhiyuan and Cheng, Anqi and Zhu, Haiyue and Li, Tianjiao and Tao, Pey Yuen and Mao, Kezhi},
title = {HFD-Teacher: High-Frequency Depth Distillation from Depth Foundation Models for Enhanced Depth Completion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {8994-9003}
}
|
Depth completion, the task of reconstructing dense depth maps from sparse depth and RGB images, plays a critical role in 3D scene understanding. However, existing methods often struggle to recover high-frequency details, such as regions with fine structures or weak signals, since depth sensors may fail to capture accurate depth maps in those regions, leading to imperfect supervision ground truth. To overcome this limitation, it is essential to introduce an alternative training source for the models. Emerging depth foundation models excel at producing high-frequency details from RGB images, yet their depth maps suffer from inconsistent scaling. Therefore, we propose a novel teacher-student framework that enhances depth completion by distilling high-frequency knowledge from depth foundation models across multiple scales. Our approach introduces two key innovations: Adaptive Local Wavelet Decomposition, which dynamically adjusts wavelet decomposition level based on local complexity for efficient feature extraction, and Topological Constraints, which apply persistent homology to enforce structural coherence and suppress spurious depth edges. Experiment results demonstrate that our method outperforms state-of-the-art methods, preserving high-frequency details and overall depth fidelity.
| null | null | null | null |
[] |
[] |
[] |
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] |
358
|
EAMamba: Efficient All-Around Vision State Space Model for Image Restoration
|
[
"Yu-Cheng Lin",
"Yu-Syuan Xu",
"Hao-Wei Chen",
"Hsien-Kai Kuo",
"Chun-Yi Lee"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Lin_EAMamba_Efficient_All-Around_Vision_State_Space_Model_for_Image_Restoration_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Lin_EAMamba_Efficient_All-Around_Vision_State_Space_Model_for_Image_Restoration_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Lin_EAMamba_Efficient_All-Around_ICCV_2025_supplemental.zip
|
@InProceedings{Lin_2025_ICCV,
author = {Lin, Yu-Cheng and Xu, Yu-Syuan and Chen, Hao-Wei and Kuo, Hsien-Kai and Lee, Chun-Yi},
title = {EAMamba: Efficient All-Around Vision State Space Model for Image Restoration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11708-11719}
}
|
Image restoration is a key task in low-level computer vision that aims to reconstruct high-quality images from degraded inputs. The emergence of Vision Mamba, which draws inspiration from the advanced state space model Mamba, marks a significant advancement in this field. Vision Mamba demonstrates excellence in modeling long-range dependencies with linear complexity, a crucial advantage for image restoration tasks. Despite its strengths, Vision Mamba encounters challenges in low-level vision tasks, including computational complexity that scales with the number of scanning sequences and local pixel forgetting. To address these limitations, this study introduces Efficient All-Around Mamba (EAMamba), an enhanced framework that incorporates a Multi-Head Selective Scan Module (MHSSM) with an all-around scanning mechanism. MHSSM efficiently aggregates multiple scanning sequences, which avoids increases in computational complexity and parameter count. The all-around scanning strategy implements multiple patterns to capture holistic information and resolves the local pixel forgetting issue. Our experimental evaluations validate these innovations across several restoration tasks, including super resolution, denoising, deblurring, and dehazing. The results validate that EAMamba achieves a significant 31-89% reduction in FLOPs while maintaining favorable performance compared to existing low-level Vision Mamba methods.
|
2506.22246
|
ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
359
|
AHCPTQ: Accurate and Hardware-Compatible Post-Training Quantization for Segment Anything Model
|
[
"Wenlun Zhang",
"Yunshan Zhong",
"Shimpei Ando",
"Kentaro Yoshioka"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_AHCPTQ_Accurate_and_Hardware-Compatible_Post-Training_Quantization_for_Segment_Anything_Model_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_AHCPTQ_Accurate_and_Hardware-Compatible_Post-Training_Quantization_for_Segment_Anything_Model_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_AHCPTQ_Accurate_and_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Wenlun and Zhong, Yunshan and Ando, Shimpei and Yoshioka, Kentaro},
title = {AHCPTQ: Accurate and Hardware-Compatible Post-Training Quantization for Segment Anything Model},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {22383-22392}
}
|
The Segment Anything Model (SAM) has demonstrated strong versatility across various visual tasks. However, its large storage requirements and high computational cost pose challenges for practical deployment. Post-training quantization (PTQ) has emerged as an effective strategy for efficient deployment, but we identify two key challenges in SAM that hinder the effectiveness of existing PTQ methods: the heavy-tailed and skewed distribution of post-GELU activations, and significant inter-channel variation in linear projection activations. To address these challenges, we propose AHCPTQ, an accurate and hardware-efficient PTQ method for SAM. AHCPTQ introduces hardware-compatible Hybrid Log-Uniform Quantization (HLUQ) to manage post-GELU activations, employing log2 quantization for dense small values and uniform quantization for sparse large values to enhance quantization resolution. Additionally, AHCPTQ incorporates Channel-Aware Grouping (CAG) to mitigate inter-channel variation by progressively clustering activation channels with similar distributions, enabling them to share quantization parameters and improving hardware efficiency. The combination of HLUQ and CAG not only enhances quantization effectiveness but also ensures compatibility with efficient hardware execution. For instance, under the W4A4 configuration on the SAM-L model, AHCPTQ achieves 36.6% mAP on instance segmentation with the DINO detector, while achieving a 7.89x speedup and 8.64x energy efficiency over its floating-point counterpart in FPGA implementation.
|
2503.03088
|
Accepted to ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
360
|
WonderTurbo: Generating Interactive 3D World in 0.72 Seconds
|
[
"Chaojun Ni",
"Xiaofeng Wang",
"Zheng Zhu",
"Weijie Wang",
"Haoyun Li",
"Guosheng Zhao",
"Jie Li",
"Wenkang Qin",
"Guan Huang",
"Wenjun Mei"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ni_WonderTurbo_Generating_Interactive_3D_World_in_0.72_Seconds_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ni_WonderTurbo_Generating_Interactive_3D_World_in_0.72_Seconds_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ni_WonderTurbo_Generating_Interactive_ICCV_2025_supplemental.pdf
|
@InProceedings{Ni_2025_ICCV,
author = {Ni, Chaojun and Wang, Xiaofeng and Zhu, Zheng and Wang, Weijie and Li, Haoyun and Zhao, Guosheng and Li, Jie and Qin, Wenkang and Huang, Guan and Mei, Wenjun},
title = {WonderTurbo: Generating Interactive 3D World in 0.72 Seconds},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27423-27434}
}
|
Interactive 3D generation is gaining momentum and capturing extensive attention for its potential to create immersive virtual experiences. However, a critical challenge in current 3D generation technologies lies in achieving real-time interactivity. To address this issue, we introduce WonderTurbo, the first real-time interactive 3D scene generation framework capable of generating novel perspectives of 3D scenes within 0.72 seconds. Specifically, WonderTurbo accelerates both geometric and appearance modeling in 3D scene generation. In terms of geometry, we propose StepSplat, an innovative method that constructs efficient 3D geometric representations through dynamic updates, each taking only 0.26 seconds. Additionally, we design QuickDepth, a lightweight depth completion module that provides consistent depth input for StepSplat, further enhancing geometric accuracy. For appearance modeling, we develop FastPaint, a 2-steps diffusion model tailored for instant inpainting, which focuses on maintaining spatial appearance consistency. Experimental results demonstrate that WonderTurbo achieves a remarkable 15xspeedup compared to baseline methods, while preserving excellent spatial consistency and delivering high-quality output.
|
2504.02261
|
Project Page: https://wonderturbo.github.io
| null |
https://wonderturbo.github.io/
|
[] |
[] |
[] |
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] |
361
|
Learning Hierarchical Line Buffer for Image Processing
|
[
"Jiacheng Li",
"Feiran Li",
"Daisuke Iso"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_Learning_Hierarchical_Line_Buffer_for_Image_Processing_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_Learning_Hierarchical_Line_Buffer_for_Image_Processing_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_Learning_Hierarchical_Line_ICCV_2025_supplemental.pdf
|
@InProceedings{Li_2025_ICCV,
author = {Li, Jiacheng and Li, Feiran and Iso, Daisuke},
title = {Learning Hierarchical Line Buffer for Image Processing},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11132-11141}
}
|
In recent years, neural networks have achieved significant progress in offline image processing. However, in online scenarios, particularly in on-chip implementations, memory usage emerges as a critical bottleneck due to the limited memory resources of integrated image processors. In this study, we focus on reducing the memory footprint of neural networks for on-chip image processing by optimizing network design for efficient memory utilization. Specifically, we consider a typical scenario in which images outputted from an image sensor are processed sequentially using line buffers in a line-by-line manner. This setting necessitates the modeling of both intra-line and inter-line correlations--capturing dependencies among pixels within a single line group and across different line groups, respectively.To model intra-line correlations, we propose a progressive feature enhancement strategy, where line pixels are processed with expanding strip convolutions in multiple stages.For inter-line correlation modeling, we introduce a hierarchical line buffer formulation, where features extracted from previous lines are incrementally reused and compressed across multiple hierarchical levels.Comprehensive experiments on various image processing tasks, including RAW denoising, Gaussian denoising, and super-resolution, demonstrate that the proposed method achieves a superior trade-off between performance and memory efficiency than previous solutions, e.g., up to 1dB PSNR gain in RAW denoising at one-fifth of peak memory usage.
| null | null | null | null |
[] |
[] |
[] |
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] |
362
|
Looking in the Mirror: A Faithful Counterfactual Explanation Method for Interpreting Deep Image Classification Models
|
[
"Townim Chowdhury",
"Vu Minh Hieu Phan",
"Kewen Liao",
"Nanyu Dong",
"Minh-Son To",
"Anton van den Hengel",
"Johan W. Verjans",
"Zhibin Liao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chowdhury_Looking_in_the_Mirror_A_Faithful_Counterfactual_Explanation_Method_for_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chowdhury_Looking_in_the_Mirror_A_Faithful_Counterfactual_Explanation_Method_for_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chowdhury_Looking_in_the_ICCV_2025_supplemental.zip
|
@InProceedings{Chowdhury_2025_ICCV,
author = {Chowdhury, Townim and Phan, Vu Minh Hieu and Liao, Kewen and Dong, Nanyu and To, Minh-Son and van den Hengel, Anton and Verjans, Johan W. and Liao, Zhibin},
title = {Looking in the Mirror: A Faithful Counterfactual Explanation Method for Interpreting Deep Image Classification Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2239-2249}
}
|
Counterfactual explanations (CFE) for deep image classifiers aim to reveal how minimal input changes lead to different model decisions, providing critical insights for model interpretation and improvement. However, existing CFE methods often rely on additional image encoders and generative models to create plausible images, neglecting the classifier's own feature space and decision boundaries. As such, they do not explain the intrinsic feature space and decision boundaries learned by the classifier. To address this limitation, we propose Mirror-CFE, a novel method that generates faithful counterfactual explanations by operating directly in the classifier's feature space, treating decision boundaries as mirrors that "reflect" feature representations in the mirror. Mirror-CFE learns a mapping function from feature space to image space while preserving distance relationships, enabling smooth transitions between source images and their counterfactuals. Through extensive experiments on four image datasets, we demonstrate that Mirror-CFE achieves superior performance in validity while maintaining input resemblance compared to state-of-the-art explanation methods. Finally, mirror-CFE provides interpretable visualization of the classifier's decision process by generating step-wise transitions that reveal how features evolve as classification confidence changes.
|
2509.16822
| null | null | null |
[] |
[] |
[] |
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] |
363
|
Inference-Time Diffusion Model Distillation
|
[
"Geon Yeong Park",
"Sang Wan Lee",
"Jong Chul Ye"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Park_Inference-Time_Diffusion_Model_Distillation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Park_Inference-Time_Diffusion_Model_Distillation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Park_Inference-Time_Diffusion_Model_ICCV_2025_supplemental.pdf
|
@InProceedings{Park_2025_ICCV,
author = {Park, Geon Yeong and Lee, Sang Wan and Ye, Jong Chul},
title = {Inference-Time Diffusion Model Distillation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4049-4058}
}
|
Diffusion distillation models effectively accelerate reverse sampling by compressing the process into fewer steps. However, these models still exhibit a performance gap compared to their pre-trained diffusion model counterparts, exacerbated by distribution shifts and accumulated errors during multi-step sampling. To address this, we introduce Distillation++, a novel inference-time distillation framework that reduces this gap by incorporating teacher-guided refinement during sampling. Inspired by recent advances in conditional sampling, our approach recasts student model sampling as a proximal optimization problem with a score distillation sampling loss (SDS). To this end, we integrate distillation optimization during reverse sampling, which can be viewed as teacher guidance that drives student sampling trajectory towards the clean manifold using pre-trained diffusion models. Thus, Distillation++ improves the denoising process in real-time without additional source data or fine-tuning. Distillation++ demonstrates substantial improvements over state-of-the-art distillation baselines, particularly in early sampling stages, positioning itself as a robust guided sampling process crafted for diffusion distillation models.
|
2412.08871
|
Code: https://github.com/geonyeong-park/inference_distillation
|
https://github.com/geonyeong-park/inference_distillation
| null |
[] |
[] |
[] |
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] |
364
|
S3E: Self-Supervised State Estimation for Radar-Inertial System
|
[
"Shengpeng Wang",
"Yulong Xie",
"Qing Liao",
"Wei Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_S3E_Self-Supervised_State_Estimation_for_Radar-Inertial_System_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_S3E_Self-Supervised_State_Estimation_for_Radar-Inertial_System_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_S3E_Self-Supervised_State_ICCV_2025_supplemental.zip
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Shengpeng and Xie, Yulong and Liao, Qing and Wang, Wei},
title = {S3E: Self-Supervised State Estimation for Radar-Inertial System},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26686-26695}
}
|
Millimeter-wave radar for state estimation is gaining significant attention for its affordability and reliability in harsh conditions. Existing localization solutions typically rely on post-processed radar point clouds as landmark points. Nonetheless, the inherent sparsity of radar point clouds, ghost points from multi-path effects, and limited angle resolution in single-chirp radar severely degrade state estimation performance. To address these issues, we propose S^3E, a Self-Supervised State Estimator that employs more richly informative radar signal spectra to bypass sparse points and fuses complementary inertial information to achieve accurate localization. S^3E fully explores the association between exteroceptive radar and proprioceptive inertial sensor to achieve complementary benefits. To deal with limited angle resolution, we introduce a novel cross-fusion technique that enhances spatial structure information by exploiting subtle rotational shift correlations across heterogeneous data. The experimental results demonstrate our method achieves robust and accurate performance without relying on localization ground truth supervision. To the best of our knowledge, this is the first attempt to achieve state estimation by fusing radar spectra and inertial data in a complementary self-supervised manner.
| null | null | null | null |
[] |
[] |
[] |
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] |
365
|
Rethinking Key-frame-based Micro-expression Recognition: A Robust and Accurate Framework Against Key-frame Errors
|
[
"Zheyuan Zhang",
"Weihao Tang",
"Hong Chen"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_Rethinking_Key-frame-based_Micro-expression_Recognition_A_Robust_and_Accurate_Framework_Against_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_Rethinking_Key-frame-based_Micro-expression_Recognition_A_Robust_and_Accurate_Framework_Against_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_Rethinking_Key-frame-based_Micro-expression_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Zheyuan and Tang, Weihao and Chen, Hong},
title = {Rethinking Key-frame-based Micro-expression Recognition: A Robust and Accurate Framework Against Key-frame Errors},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {12274-12283}
}
|
Micro-expression recognition (MER) is a highly challenging task in affective computing. With the reduced-sized micro-expression (ME) input that contains key information based on key-frame indexes, key-frame-based methods have significantly improved the performance of MER. However, most of these methods focus on improving the performance with relatively accurate key-frame indexes, while ignoring the difficulty of obtaining accurate key-frame indexes and the objective existence of key-frame index errors, which impedes them from moving towards practical applications. In this paper, we propose CausalNet, a novel framework to achieve robust MER facing key-frame index errors while maintaining accurate recognition. To enhance robustness, CausalNet takes the representation of the entire ME sequence as the input. To address the information redundancy brought by the complete ME range input and maintain accurate recognition, first, the Causal Motion Position Learning Module (CMPLM) is proposed to help the model locate the muscle movement areas related to Action Units (AUs), thereby reducing the attention to other redundant areas. Second, the Causal Attention Block (CAB) is proposed to deeply learn the causal relationships between the muscle contraction and relaxation movements in MEs. Empirical experiments have demonstrated that on popular ME benchmarks, the CausalNet has achieved robust MER under different levels of key-frame index noise. Meanwhile, it has surpassed state-of-the-art (SOTA) methods on several standard MER benchmarks when using the provided annotated key-frames. Code is available at https://github.com/tony19980810/CausalNet.
|
2508.06640
| null |
https://github.com/tony19980810/CausalNet
| null |
[] |
[] |
[] |
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366
|
Target Bias Is All You Need: Zero-Shot Debiasing of Vision-Language Models with Bias Corpus
|
[
"Taeuk Jang",
"Hoin Jung",
"Xiaoqian Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Jang_Target_Bias_Is_All_You_Need_Zero-Shot_Debiasing_of_Vision-Language_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Jang_Target_Bias_Is_All_You_Need_Zero-Shot_Debiasing_of_Vision-Language_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Jang_Target_Bias_Is_ICCV_2025_supplemental.pdf
|
@InProceedings{Jang_2025_ICCV,
author = {Jang, Taeuk and Jung, Hoin and Wang, Xiaoqian},
title = {Target Bias Is All You Need: Zero-Shot Debiasing of Vision-Language Models with Bias Corpus},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1935-1946}
}
|
Vision-Language Models (VLMs) like CLIP have shown remarkable zero-shot performance by aligning different modalities in the embedding space, enabling diverse applications from image editing to visual question answering (VQA). However, these models often inherit biases from their training data, resulting in performance disparities across specific subpopulations. Traditional debiasing methods for VLMs primarily focus on specific downstream tasks using labeled datasets, which we argue is insufficient given the broad applicability of VLMs. Specifically, these methods struggle with generalizability, transferability, and feasibility due to overfitting, limited task applicability, and regulatory constraints on the use of sensitive data, making them less practical in real-world scenarios. To address these challenges, we propose a novel task-agnostic method for learning debiased image embeddings in VLMs. Our approach does not require expensive annotated datasets or curated prompts for downstream tasks, while still preserving the inherent zero-shot capabilities of these models. Instead, we leverage easily accessible information: 1) a bias text corpus generated by a large language model, and 2) a generic unsupervised vision dataset. Our method disentangles the image embedding into bias and neutral components by applying centered kernel alignment (CKA) regularization to the text-vision representational similarity, using the bias text corpus over the generic vision dataset. Experimental results validate the effectiveness of our approach across multiple tasks, offering a practical and versatile solution to debiasing VLMs.
| null | null | null | null |
[] |
[] |
[] |
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-0.015508104115724564,
-0.022620858624577522,
-0.05192290246486664,
0.03427579998970032,
-0.04278320074081421,
-0.004992544651031494,
-0.04695567116141319,
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0.041234008967876434,
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0.02562559023499489,
0.009951885789632797
] |
367
|
Mastering Collaborative Multi-modal Data Selection: A Focus on Informativeness, Uniqueness, and Representativeness
|
[
"Qifan Yu",
"Zhebei Shen",
"Zhongqi Yue",
"Yang Wu",
"Bosheng Qin",
"Wenqiao Zhang",
"Yunfei Li",
"Juncheng Li",
"Siliang Tang",
"Yueting Zhuang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yu_Mastering_Collaborative_Multi-modal_Data_Selection_A_Focus_on_Informativeness_Uniqueness_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yu_Mastering_Collaborative_Multi-modal_Data_Selection_A_Focus_on_Informativeness_Uniqueness_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yu_Mastering_Collaborative_Multi-modal_ICCV_2025_supplemental.pdf
|
@InProceedings{Yu_2025_ICCV,
author = {Yu, Qifan and Shen, Zhebei and Yue, Zhongqi and Wu, Yang and Qin, Bosheng and Zhang, Wenqiao and Li, Yunfei and Li, Juncheng and Tang, Siliang and Zhuang, Yueting},
title = {Mastering Collaborative Multi-modal Data Selection: A Focus on Informativeness, Uniqueness, and Representativeness},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {155-165}
}
|
Instruction tuning fine-tunes pre-trained Multi-modal Large Language Models (MLLMs) to handle real-world tasks. However, the rapid expansion of visual instruction datasets introduces data redundancy, leading to excessive computational costs. We propose a collaborative framework, DataTailor, which leverages three key principles--informativeness, uniqueness, and representativeness--for effective data selection. We argue that a valuable sample should be informative of the task, non-redundant, and represent the sample distribution (i.e., not an outlier). We further propose practical ways to score against each principle, which automatically adapts to a given dataset without tedious hyperparameter tuning. Comprehensive experiments on various benchmarks demonstrate that DataTailor achieves 101.3% of the performance of full-data fine-tuning with only 15% of the data, significantly reducing computational costs while maintaining superior results. This exemplifies the "Less is More" philosophy in MLLM development. The code and data is available in this URL.
|
2412.06293
|
ICCV 2025 Highlight
|
https://github.com/Yuqifan1117/DataTailor
| null |
[] |
[] |
[] |
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] |
368
|
WIR3D: Visually-Informed and Geometry-Aware 3D Shape Abstraction
|
[
"Richard Liu",
"Daniel Fu",
"Noah Tan",
"Itai Lang",
"Rana Hanocka"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_WIR3D_Visually-Informed_and_Geometry-Aware_3D_Shape_Abstraction_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_WIR3D_Visually-Informed_and_Geometry-Aware_3D_Shape_Abstraction_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_WIR3D_Visually-Informed_and_ICCV_2025_supplemental.zip
|
@InProceedings{Liu_2025_ICCV,
author = {Liu, Richard and Fu, Daniel and Tan, Noah and Lang, Itai and Hanocka, Rana},
title = {WIR3D: Visually-Informed and Geometry-Aware 3D Shape Abstraction},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {14810-14821}
}
|
In this work we present WIR3D, a technique for abstracting 3D shapes through a sparse set of visually meaningful curves in 3D. We optimize the parameters of Bezier curves such that they faithfully represent both the geometry and salient visual features (e.g. texture) of the shape from arbitrary viewpoints. We leverage the intermediate activations of a pre-trained foundation model (CLIP) to guide our optimization process. We divide our optimization into two phases: one for capturing the coarse geometry of the shape, and the other for representing fine-grained features. Our second phase supervision is spatially guided by a novel localized keypoint loss. This spatial guidance enables user control over abstracted features. We ensure fidelity to the original surface through a neural SDF loss, which allows the curves to be used as intuitive deformation handles. We successfully apply our method for shape abstraction over a broad dataset of shapes with varying complexity, geometric structure, and texture, and demonstrate downstream applications for feature control and shape deformation.
|
2505.04813
|
ICCV 2025 Oral Project page: https://threedle.github.io/wir3d/
| null | null |
[] |
[] |
[] |
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] |
369
|
XTrack: Multimodal Training Boosts RGB-X Video Object Trackers
|
[
"Yuedong Tan",
"Zongwei Wu",
"Yuqian Fu",
"Zhuyun Zhou",
"Guolei Sun",
"Eduard Zamfir",
"Chao Ma",
"Danda Paudel",
"Luc Van Gool",
"Radu Timofte"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Tan_XTrack_Multimodal_Training_Boosts_RGB-X_Video_Object_Trackers_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Tan_XTrack_Multimodal_Training_Boosts_RGB-X_Video_Object_Trackers_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Tan_XTrack_Multimodal_Training_ICCV_2025_supplemental.zip
|
@InProceedings{Tan_2025_ICCV,
author = {Tan, Yuedong and Wu, Zongwei and Fu, Yuqian and Zhou, Zhuyun and Sun, Guolei and Zamfir, Eduard and Ma, Chao and Paudel, Danda and Van Gool, Luc and Timofte, Radu},
title = {XTrack: Multimodal Training Boosts RGB-X Video Object Trackers},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {5734-5744}
}
|
Multimodal sensing has proven valuable for visual tracking, as different sensor types offer unique strengths in handling one specific challenging scene where object appearance varies. While a generalist model capable of leveraging all modalities would be ideal, development is hindered by data sparsity, typically in practice, only one modality is available at a time. Therefore, it is crucial to ensure and achieve that knowledge gained from multimodal sensing -- such as identifying relevant features and regions -- is effectively shared, even when certain modalities are unavailable at inference. We venture with a simple assumption: similar samples across different modalities have more knowledge to share than otherwise. To implement this, we employ a classifier with weak loss tasked with distinguishing between modalities. More specifically, if the classifier "fails" to accurately identify the modality of the given sample, this signals an opportunity for cross-modal knowledge sharing. Intuitively, knowledge transfer is facilitated whenever a sample from one modality is sufficiently close and aligned with another. Technically, we achieve this by routing samples from one modality to the expert of the others, within a mixture-of-experts framework designed for multimodal video object tracking. During the inference, the expert of the respective modality is chosen, which we show to benefit from the multimodal knowledge available during training, thanks to the proposed method. Through the exhaustive experiments that use only paired RGB-E, RGB-D, and RGB-T during training, we showcase the benefit of the proposed method for RGB-X tracker during inference, with an average +3% precision improvement over the current SOTA. The source code is publicly available at https://github.com/supertyd/XTrack.
| null | null | null | null |
[] |
[] |
[] |
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] |
370
|
FaceCraft4D: Animated 3D Facial Avatar Generation from a Single Image
|
[
"Fei Yin",
"Mallikarjun B R",
"Chun-Han Yao",
"Rafal K. Mantiuk",
"Varun Jampani"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yin_FaceCraft4D_Animated_3D_Facial_Avatar_Generation_from_a_Single_Image_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yin_FaceCraft4D_Animated_3D_Facial_Avatar_Generation_from_a_Single_Image_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yin_FaceCraft4D_Animated_3D_ICCV_2025_supplemental.zip
|
@InProceedings{Yin_2025_ICCV,
author = {Yin, Fei and R, Mallikarjun B and Yao, Chun-Han and Mantiuk, Rafal K. and Jampani, Varun},
title = {FaceCraft4D: Animated 3D Facial Avatar Generation from a Single Image},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11612-11621}
}
|
We present a novel framework for generating high-quality, animatable 4D avatar from a single image. While recent advances have shown promising results in 4D avatar creation, existing methods either require extensive multiview data or struggle with geometry accuracy and identity consistency. To address these limitations, we propose a comprehensive system that leverages geometry, image, and video priors to create full-view, animatable avatars. Our approach first obtains initial coarse geometry through 3D-GAN inversion. Then, it enhances multiview textures using depth-guided warping signals for cross-view consistency with the help of the image diffusion model. To handle expression animation, we incorporate a video prior with synchronized driving signals across viewpoints. We further introduce a Consistent-Inconsistent training to effectively handle data inconsistencies during 4D reconstruction. Experimental results demonstrate that our method achieves superior quality compared to the prior art, while maintaining consistency across different viewpoints and expressions.
|
2504.15179
| null | null | null |
[] |
[] |
[] |
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] |
371
|
GenFlow3D: Generative Scene Flow Estimation and Prediction on Point Cloud Sequences
|
[
"Hanlin Li",
"Wenming Weng",
"Yueyi Zhang",
"Zhiwei Xiong"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_GenFlow3D_Generative_Scene_Flow_Estimation_and_Prediction_on_Point_Cloud_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_GenFlow3D_Generative_Scene_Flow_Estimation_and_Prediction_on_Point_Cloud_ICCV_2025_paper.pdf
| null |
@InProceedings{Li_2025_ICCV,
author = {Li, Hanlin and Weng, Wenming and Zhang, Yueyi and Xiong, Zhiwei},
title = {GenFlow3D: Generative Scene Flow Estimation and Prediction on Point Cloud Sequences},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27488-27497}
}
|
Scene flow provides the fundamental information of the scene dynamics. Existing scene flow estimation methods typically rely on the correlation between only a consecutive point cloud pair, which makes them limited to the instantaneous state of the scene and face challenges in real-world scenarios with factors like occlusion, noise, and diverse motion of background and foreground. In this paper, we study the joint sequential scene flow estimation and future scene flow prediction on point cloud sequences. The expanded sequential input introduces long-term and high-order motion information. We propose GenFlow3D, a recurrent neural network model which integrates diffusion in the decoder to better incorporate the two tasks and enhance the ability to extract general motion patterns. A transformer-based denoising network is adopted to help capture useful information. Depending on the input point clouds, discriminative condition signals are generated to guide the diffusion decoder to switch among different modes specific for scene flow estimation and prediction in a multi-scale manner. GenFlow3D is evaluated on the real-world datasets nuScenes and Argoverse 2, and demonstrates superior performance compared with the existing methods. Our code is available at https://github.com/ustc-hlli/GenFlow3D.
| null | null | null | null |
[] |
[] |
[] |
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] |
372
|
Attention to Neural Plagiarism: Diffusion Models Can Plagiarize Your Copyrighted Images!
|
[
"Zihang Zou",
"Boqing Gong",
"Liqiang Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zou_Attention_to_Neural_Plagiarism_Diffusion_Models_Can_Plagiarize_Your_Copyrighted_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zou_Attention_to_Neural_Plagiarism_Diffusion_Models_Can_Plagiarize_Your_Copyrighted_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zou_Attention_to_Neural_ICCV_2025_supplemental.pdf
|
@InProceedings{Zou_2025_ICCV,
author = {Zou, Zihang and Gong, Boqing and Wang, Liqiang},
title = {Attention to Neural Plagiarism: Diffusion Models Can Plagiarize Your Copyrighted Images!},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {19546-19556}
}
|
In this paper, we highlight a critical threat posed by emerging neural models--data plagiarism. We demonstrate how modern neural models (e.g., diffusion models) can effortlessly replicate copyrighted images, even when protected by advanced watermarking techniques. To expose the vulnerability in copyright protection and facilitate future research, we propose a general approach regarding neural plagiarism that can either forge replicas of copyrighted data or introduce copyright ambiguity. Our method, based on "anchors and shims", employs inverse latents as anchors and finds shim perturbations that can gradually deviate the anchor latents, thereby evading watermark or copyright detection. By applying perturbation to the cross-attention mechanism at different timesteps, our approach induces varying degrees of semantic modifications in copyrighted images, making it to bypass protections ranging from visible trademarks, signatures to invisible watermarks. Notably, our method is a purely gradient-based search that requires no additional training or fine-tuning. Empirical experiments on MS-COCO and real-world copyrighted images show that diffusion models can replicate copyrighted images, underscoring the urgent need for countermeasures against neural plagiarism. Source code is available at: https://github.com/zzzucf/Neural-Plagiarism.
| null | null | null | null |
[] |
[] |
[] |
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] |
373
|
PBCAT: Patch-Based Composite Adversarial Training against Physically Realizable Attacks on Object Detection
|
[
"Xiao Li",
"Yiming Zhu",
"Yifan Huang",
"Wei Zhang",
"Yingzhe He",
"Jie Shi",
"Xiaolin Hu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_PBCAT_Patch-Based_Composite_Adversarial_Training_against_Physically_Realizable_Attacks_on_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_PBCAT_Patch-Based_Composite_Adversarial_Training_against_Physically_Realizable_Attacks_on_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_PBCAT_Patch-Based_Composite_ICCV_2025_supplemental.zip
|
@InProceedings{Li_2025_ICCV,
author = {Li, Xiao and Zhu, Yiming and Huang, Yifan and Zhang, Wei and He, Yingzhe and Shi, Jie and Hu, Xiaolin},
title = {PBCAT: Patch-Based Composite Adversarial Training against Physically Realizable Attacks on Object Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24456-24466}
}
|
Object detection plays a crucial role in many security-sensitive applications, such as autonomous driving and video surveillance. However, several recent studies have shown that object detectors can be easily fooled by physically realizable attacks, e.g., adversarial patches and recent adversarial textures, which pose realistic and urgent threats. Adversarial Training (AT) has been recognized as the most effective defense against adversarial attacks. While AT has been extensively studied in the l_infinity-bounded attack settings on classification models, AT against physically realizable attacks on object detectors has received limited exploration. Early attempts are only performed to defend against adversarial patches, leaving AT against a wider range of physically realizable attacks under-explored. In this work, we consider defending against various physically realizable attacks with a unified AT method. We propose PBCAT, a novel Patch-Based Composite Adversarial Training strategy. PBCAT optimizes the model by incorporating the combination of small-area gradient-guided adversarial patches and imperceptible global adversarial perturbations covering the entire image. With these designs, PBCAT has the potential to defend against not only adversarial patches but also unseen physically realizable attacks such as adversarial textures. Extensive experiments in multiple settings demonstrated that PBCAT significantly improved robustness against various physically realizable attacks over state-of-the-art defense methods. Notably, it improved the detection accuracy by 29.7% over previous defense methods under one recent adversarial texture attack.
|
2506.23581
|
Accepted by ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
374
|
TRCE: Towards Reliable Malicious Concept Erasure in Text-to-Image Diffusion Models
|
[
"Ruidong Chen",
"Honglin Guo",
"Lanjun Wang",
"Chenyu Zhang",
"Weizhi Nie",
"An-An Liu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_TRCE_Towards_Reliable_Malicious_Concept_Erasure_in_Text-to-Image_Diffusion_Models_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_TRCE_Towards_Reliable_Malicious_Concept_Erasure_in_Text-to-Image_Diffusion_Models_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_TRCE_Towards_Reliable_ICCV_2025_supplemental.pdf
|
@InProceedings{Chen_2025_ICCV,
author = {Chen, Ruidong and Guo, Honglin and Wang, Lanjun and Zhang, Chenyu and Nie, Weizhi and Liu, An-An},
title = {TRCE: Towards Reliable Malicious Concept Erasure in Text-to-Image Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {18927-18936}
}
|
Recent advances in text-to-image diffusion models enable photorealistic image generation, but they also risk producing malicious content, such as NSFW images. To mitigate risk, concept erasure methods are studied to facilitate the model to unlearn specific concepts. However, current studies struggle to fully erase malicious concepts implicitly embedded in prompts (e.g., metaphorical expressions or adversarial prompts) while preserving the model's normal generation capability. To address this challenge, our study proposes TRCE, using a two-stage concept erasure strategy to achieve an effective trade-off between reliable erasure and knowledge preservation. Firstly, TRCE starts by erasing the malicious semantics implicitly embedded in textual prompts. By identifying a critical mapping objective(i.e., the [EoT] embedding), we optimize the cross-attention layers to map malicious prompts to contextually similar prompts but with safe concepts. This step prevents the model from being overly influenced by malicious semantics during the denoising process. Following this, considering the deterministic properties of the sampling trajectory of the diffusion model, TRCE further steers the early denoising prediction toward the safe direction and away from the unsafe one through contrastive learning, thus further avoiding the generation of malicious content. Finally, we conduct comprehensive evaluations of TRCE on multiple malicious concept erasure benchmarks, and the results demonstrate its effectiveness in erasing malicious concepts while better preserving the model's original generation ability.
|
2503.07389
| null | null | null |
[] |
[
"ddgoodgood/trce-erased-model"
] |
[] |
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] |
375
|
PossLoss: A Reliable and Sensitive Facial Landmark Detection Loss Function
|
[
"Qikui Zhu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhu_PossLoss_A_Reliable_and_Sensitive_Facial_Landmark_Detection_Loss_Function_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhu_PossLoss_A_Reliable_and_Sensitive_Facial_Landmark_Detection_Loss_Function_ICCV_2025_paper.pdf
| null |
@InProceedings{Zhu_2025_ICCV,
author = {Zhu, Qikui},
title = {PossLoss: A Reliable and Sensitive Facial Landmark Detection Loss Function},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24858-24867}
}
|
ate and locate the peak of the heatmap, while adaptively balancing the influence of landmarks and background pixels through self-weighting, addressing the extreme imbalance between landmarks and non-landmarks. More advanced is that our PossLoss is sample-sensitive, which can distinguish easy and hard landmarks and adaptively make the model focused more on hard landmarks. Moreover, it addresses the difficulty of accurately evaluating heatmap distribution, especially in addressing small errors due to peak mismatches. We analyzed and evaluated our PossLoss on three challenging facial landmark detection tasks. The experimental results show that our PossLoss significantly improves the performance of landmark detection and outperforms the state-of-the-art methods.
| null | null | null | null |
[] |
[] |
[] |
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] |
376
|
ChartPoint: Guiding MLLMs with Grounding Reflection for Chart Reasoning
|
[
"Zhengzhuo Xu",
"SiNan Du",
"Yiyan Qi",
"Siwen Lu",
"Chengjin Xu",
"Chun Yuan",
"Jian Guo"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xu_ChartPoint_Guiding_MLLMs_with_Grounding_Reflection_for_Chart_Reasoning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xu_ChartPoint_Guiding_MLLMs_with_Grounding_Reflection_for_Chart_Reasoning_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xu_ChartPoint_Guiding_MLLMs_ICCV_2025_supplemental.pdf
|
@InProceedings{Xu_2025_ICCV,
author = {Xu, Zhengzhuo and Du, SiNan and Qi, Yiyan and Lu, Siwen and Xu, Chengjin and Yuan, Chun and Guo, Jian},
title = {ChartPoint: Guiding MLLMs with Grounding Reflection for Chart Reasoning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {426-436}
}
|
Multimodal Large Language Models (MLLMs) have emerged as powerful tools for chart comprehension. However, they heavily rely on extracted content via OCR, which leads to numerical hallucinations when chart textual annotations are sparse. While existing methods focus on scaling instructions, they fail to address the fundamental challenge, i.e., reasoning with visual perception. In this paper, we identify a critical observation: MLLMs exhibit weak grounding in chart elements and proportional relationships, as evidenced by their inability to localize key positions to match their reasoning. To bridge this gap, we propose PointCoT, which integrates reflective interaction into chain-of-thought reasoning in charts. By prompting MLLMs to generate bounding boxes and re-render charts based on location annotations, we establish connections between textual reasoning steps and visual grounding regions. We further introduce an automated pipeline to construct ChartPoint-SFT-62k, a dataset featuring 19.2K high-quality chart samples with step-by-step CoT, bounding box, and re-rendered visualizations. Leveraging this data, we develop two instruction-tuned models, ChartPoint_Q2 and ChartPoint_Q2.5, which outperform state-of-the-art across several chart benchmarks, e.g., +5.04% on ChartBench.
| null | null | null | null |
[] |
[] |
[] |
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] |
377
|
StealthAttack: Robust 3D Gaussian Splatting Poisoning via Density-Guided Illusions
|
[
"Bo-Hsu Ke",
"You-Zhe Xie",
"Yu-Lun Liu",
"Wei-Chen Chiu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ke_StealthAttack_Robust_3D_Gaussian_Splatting_Poisoning_via_Density-Guided_Illusions_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ke_StealthAttack_Robust_3D_Gaussian_Splatting_Poisoning_via_Density-Guided_Illusions_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ke_StealthAttack_Robust_3D_ICCV_2025_supplemental.pdf
|
@InProceedings{Ke_2025_ICCV,
author = {Ke, Bo-Hsu and Xie, You-Zhe and Liu, Yu-Lun and Chiu, Wei-Chen},
title = {StealthAttack: Robust 3D Gaussian Splatting Poisoning via Density-Guided Illusions},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27400-27411}
}
|
3D scene representation methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have significantly advanced novel view synthesis. As these methods become prevalent, addressing their vulnerabilities becomes critical. We analyze 3DGS robustness against image-level poisoning attacks and propose a novel density-guided poisoning method. Our method strategically injects Gaussian points into low-density regions identified via Kernel Density Estimation (KDE), embedding viewpoint-dependent illusory objects clearly visible from poisoned views while minimally affecting innocent views. Additionally, we introduce an adaptive noise strategy to disrupt multi-view consistency, further enhancing attack effectiveness. We propose a KDE-based evaluation protocol to assess attack difficulty systematically, enabling objective benchmarking for future research. Extensive experiments demonstrate our method's superior performance compared to state-of-the-art techniques.
|
2510.02314
| null | null | null |
[] |
[] |
[] |
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378
|
LightCity: An Urban Dataset for Outdoor Inverse Rendering and Reconstruction under Multi-illumination Conditions
|
[
"Jingjing Wang",
"Qirui Hu",
"Chong Bao",
"Yuke Zhu",
"Hujun Bao",
"Zhaopeng Cui",
"Guofeng Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_LightCity_An_Urban_Dataset_for_Outdoor_Inverse_Rendering_and_Reconstruction_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_LightCity_An_Urban_Dataset_for_Outdoor_Inverse_Rendering_and_Reconstruction_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_LightCity_An_Urban_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Jingjing and Hu, Qirui and Bao, Chong and Zhu, Yuke and Bao, Hujun and Cui, Zhaopeng and Zhang, Guofeng},
title = {LightCity: An Urban Dataset for Outdoor Inverse Rendering and Reconstruction under Multi-illumination Conditions},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26477-26487}
}
|
We propose an outdoor scene dataset and propose a series of benchmarks based on it.Inverse rendering in urban scenes is pivotal for applications like autonomous driving and digital twins, yet it faces significant challenges due to complex illumination conditions, including multi-illumination and indirect light and shadow effects.However, these challenges' effects on intrinsic decomposition and 3D reconstruction are not explored due to the lack of appropriate datasets. In this paper, we present LightCity, a novel high-quality synthetic urban dataset featuring diverse illumination conditions with realistic indirect light and shadow effects.LightCity encompasses over 300 sky maps with highly controllable illumination, varying scales with both street-level and aerial perspectives over 50K images, and rich properties such as depth, normal, and material components, light and indirect light, etc.Besides, we leverage LightCity to benchmark three fundamental tasks in the urban environments and conduct a comprehensive analysis of these benchmarks, laying a robust foundation for advancing related research.
| null | null | null | null |
[] |
[] |
[] |
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] |
379
|
On the Generalization of Representation Uncertainty in Earth Observation
|
[
"Spyros Kondylatos",
"Nikolaos Ioannis Bountos",
"Dimitrios Michail",
"Xiao Xiang Zhu",
"Gustau Camps-Valls",
"Ioannis Papoutsis"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Kondylatos_On_the_Generalization_of_Representation_Uncertainty_in_Earth_Observation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Kondylatos_On_the_Generalization_of_Representation_Uncertainty_in_Earth_Observation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Kondylatos_On_the_Generalization_ICCV_2025_supplemental.pdf
|
@InProceedings{Kondylatos_2025_ICCV,
author = {Kondylatos, Spyros and Bountos, Nikolaos Ioannis and Michail, Dimitrios and Zhu, Xiao Xiang and Camps-Valls, Gustau and Papoutsis, Ioannis},
title = {On the Generalization of Representation Uncertainty in Earth Observation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {6552-6562}
}
|
Recent advances in Computer Vision have introduced the concept of pretrained representation uncertainty, enabling zero-shot uncertainty estimation. This holds significant potential for Earth Observation (EO), where trustworthiness is critical, yet the complexity of EO data poses challenges to uncertainty-aware methods. In this work, we investigate the generalization of representation uncertainty in EO, considering the domain's unique semantic characteristics. We pretrain uncertainties on large EO datasets and propose an evaluation framework to assess their zero-shot performance in multi-label classification and segmentation EO tasks. Our findings reveal that, unlike uncertainties pretrained on natural images, EO-pretraining exhibits strong generalization across unseen EO domains, geographic locations, and target granularities, while maintaining sensitivity to variations in ground sampling distance. We demonstrate the practical utility of pretrained uncertainties showcasing their alignment with task-specific uncertainties in downstream tasks, their sensitivity to real-world EO image noise, and their ability to generate spatial uncertainty estimates out-of-the-box. Initiating the discussion on representation uncertainty in EO, our study provides insights into its strengths and limitations, paving the way for future research in the field. Code and weights are available at: https://github.com/Orion-AI-Lab/EOUncertaintyGeneralization.
|
2503.07082
|
18 pages
|
https://github.com/Orion-AI-Lab/EOUncertaintyGeneralization
| null |
[] |
[] |
[] |
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] |
380
|
Region-aware Anchoring Mechanism for Efficient Referring Visual Grounding
|
[
"Shuyi Ouyang",
"Ziwei Niu",
"Hongyi Wang",
"Yen-Wei Chen",
"Lanfen Lin"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ouyang_Region-aware_Anchoring_Mechanism_for_Efficient_Referring_Visual_Grounding_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ouyang_Region-aware_Anchoring_Mechanism_for_Efficient_Referring_Visual_Grounding_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ouyang_Region-aware_Anchoring_Mechanism_ICCV_2025_supplemental.pdf
|
@InProceedings{Ouyang_2025_ICCV,
author = {Ouyang, Shuyi and Niu, Ziwei and Wang, Hongyi and Chen, Yen-Wei and Lin, Lanfen},
title = {Region-aware Anchoring Mechanism for Efficient Referring Visual Grounding},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24192-24202}
}
|
Referring Visual Grounding (RVG) tasks revolve around utilizing vision-language interactions to incorporate object information from language expressions, thereby enabling targeted object detection or segmentation within images. Transformer-based methods have enabled effective interaction through attention mechanisms, achieving notable performance in RVG tasks. However, existing strategies for RVG, which involve direct interaction between visual and linguistic features, face three key challenges: (i) tendency to focus on a single target, (ii) insufficient control over linguistic noise, and (iii) high computational cost. To address these challenges, we propose a Region-aware Anchoring Mechanism (RaAM) that mediates vision-language interactions. In RaAM, region-aware anchors engage in alternating interactions with vision and language modalities, acting as indicators for object presence across different regions within the image. RaAM (i) directs attention to multiple target regions for better localization, (ii) reduces cross-modal redundancy by using anchors as buffers, and (iii) lowers time complexity. In addition, we design region and pixel level loss functions to enhance object presence assessment and edge precision. We evaluate our RaAM-RVG on four benchmark datasets and integrate RaAM into various models by replacing their interaction design. Results show that RaAM outperforms state-of-the-art methods with lower computational cost.
| null | null | null | null |
[] |
[] |
[] |
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] |
381
|
Mind the Cost of Scaffold! Benign Clients May Even Become Accomplices of Backdoor Attack
|
[
"Xingshuo Han",
"Xuanye Zhang",
"Xiang Lan",
"Haozhao Wang",
"Shengmin Xu",
"Shen Ren",
"Jason Zeng",
"Ming Wu",
"Michael Heinrich",
"Tianwei Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Han_Mind_the_Cost_of_Scaffold_Benign_Clients_May_Even_Become_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Han_Mind_the_Cost_of_Scaffold_Benign_Clients_May_Even_Become_ICCV_2025_paper.pdf
| null |
@InProceedings{Han_2025_ICCV,
author = {Han, Xingshuo and Zhang, Xuanye and Lan, Xiang and Wang, Haozhao and Xu, Shengmin and Ren, Shen and Zeng, Jason and Wu, Ming and Heinrich, Michael and Zhang, Tianwei},
title = {Mind the Cost of Scaffold! Benign Clients May Even Become Accomplices of Backdoor Attack},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1580-1589}
}
|
By using a control variate to calibrate the local gradient of each client, Scaffold has been widely known as a powerful solution to mitigate the impact of data heterogeneity in Federated Learning. Although Scaffold achieves significant performance improvements, we show that this superiority is at the cost of increased security vulnerabilities. Specifically, this paper presents BadSFL, the first backdoor attack targeting Scaffold, which turns benign clients into accomplices to amplify the attack effect. The core idea of BadSFL is to uniquely tamper with the control variate to subtly steer benign clients' local gradient updates towards the attacker's poisoned direction, effectively turning them into unwitting accomplices, significantly enhancing the backdoor persistence. Additionally, BadSFL leverages a GAN-enhanced poisoning strategy to enrich the attacker's dataset, maintaining high accuracy on both benign and backdoored samples while remaining stealthy. Extensive experiments demonstrate that BadSFL achieves superior attack durability, maintaining effectiveness for over 60 global rounds--lasting up to three times longer than existing baselines even after ceasing malicious model injections.
|
2411.16167
| null | null | null |
[] |
[] |
[] |
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382
|
MV-Adapter: Multi-View Consistent Image Generation Made Easy
|
[
"Zehuan Huang",
"Yuan-Chen Guo",
"Haoran Wang",
"Ran Yi",
"Lizhuang Ma",
"Yan-Pei Cao",
"Lu Sheng"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Huang_MV-Adapter_Multi-View_Consistent_Image_Generation_Made_Easy_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Huang_MV-Adapter_Multi-View_Consistent_Image_Generation_Made_Easy_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Huang_MV-Adapter_Multi-View_Consistent_ICCV_2025_supplemental.pdf
|
@InProceedings{Huang_2025_ICCV,
author = {Huang, Zehuan and Guo, Yuan-Chen and Wang, Haoran and Yi, Ran and Ma, Lizhuang and Cao, Yan-Pei and Sheng, Lu},
title = {MV-Adapter: Multi-View Consistent Image Generation Made Easy},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {16377-16387}
}
|
Existing multi-view image generation methods often make invasive modifications to pre-trained text-to-image (T2I) models and require full fine-tuning, leading to high computational costs and degradation in image quality due to scarce high-quality 3D data. This paper introduces MV-Adapter, an efficient and versatile adapter that enhances T2I models and their derivatives without altering the original network structure or feature space. To efficiently model the 3D geometric knowledge within the adapter, we introduce innovative designs that include duplicated self-attention layers and parallel attention architecture, enabling the adapter to inherit the powerful priors of the pre-trained models to model the novel 3D knowledge. Moreover, we present a unified condition encoder that seamlessly integrates camera parameters and geometric information, facilitating applications such as text- and image-based 3D generation and texturing. MV-Adapter achieves multi-view generation at 768 resolution on Stable Diffusion XL (SDXL), and demonstrates adaptability and versatility. It can also be extended to arbitrary view generation, enabling broader applications. We demonstrate that MV-Adapter sets a new quality standard for multi-view image generation, and opens up new possibilities due to its efficiency, adaptability and versatility.
| null | null | null | null |
[] |
[] |
[] |
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] |
383
|
Hybrid-Tower: Fine-grained Pseudo-query Interaction and Generation for Text-to-Video Retrieval
|
[
"Bangxiang Lan",
"Ruobing Xie",
"Ruixiang Zhao",
"Xingwu Sun",
"Zhanhui Kang",
"Gang Yang",
"Xirong Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Lan_Hybrid-Tower_Fine-grained_Pseudo-query_Interaction_and_Generation_for_Text-to-Video_Retrieval_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Lan_Hybrid-Tower_Fine-grained_Pseudo-query_Interaction_and_Generation_for_Text-to-Video_Retrieval_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Lan_Hybrid-Tower_Fine-grained_Pseudo-query_ICCV_2025_supplemental.pdf
|
@InProceedings{Lan_2025_ICCV,
author = {Lan, Bangxiang and Xie, Ruobing and Zhao, Ruixiang and Sun, Xingwu and Kang, Zhanhui and Yang, Gang and Li, Xirong},
title = {Hybrid-Tower: Fine-grained Pseudo-query Interaction and Generation for Text-to-Video Retrieval},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24497-24506}
}
|
The Text-to-Video Retrieval (T2VR) task aims to retrieve unlabeled videos by textual queries with the same semantic meanings. Recent CLIP-based approaches have explored two frameworks: Two-Tower versus Single-Tower framework, yet the former suffers from low effectiveness, while the latter suffers from low efficiency. In this study, we explore a new Hybrid-Tower framework that can hybridize the advantages of the Two-Tower and Single-Tower framework, achieving high effectiveness and efficiency simultaneously. We propose a novel hybrid method, Fine-grained Pseudo-query Interaction and Generation for T2VR, i.e. \name , which includes a new pseudo-query generator designed to generate a pseudo-query for each video. This enables the video feature and the textual features of pseudo-query to interact in a fine-grained manner, similar to the Single-Tower approaches to hold high effectiveness, even before the real textual query is received. Simultaneously, our method introduces no additional storage or computational overhead compared to the Two-Tower framework during the inference stage, thus maintaining high efficiency. Extensive experiments on five commonly used text-video retrieval benchmarks demonstrate that our method achieves a significant improvement over the baseline, with an increase of 1.6% ~ 3.9% in R@1. Furthermore, our method matches the efficiency of Two-Tower models while achieving near state-of-the-art performance, highlighting the advantages of the Hybrid-Tower framework.
| null | null | null | null |
[] |
[] |
[] |
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] |
384
|
ATLAS: Decoupling Skeletal and Shape Parameters for Expressive Parametric Human Modeling
|
[
"Jinhyung Park",
"Javier Romero",
"Shunsuke Saito",
"Fabian Prada",
"Takaaki Shiratori",
"Yichen Xu",
"Federica Bogo",
"Shoou-I Yu",
"Kris Kitani",
"Rawal Khirodkar"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Park_ATLAS_Decoupling_Skeletal_and_Shape_Parameters_for_Expressive_Parametric_Human_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Park_ATLAS_Decoupling_Skeletal_and_Shape_Parameters_for_Expressive_Parametric_Human_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Park_ATLAS_Decoupling_Skeletal_ICCV_2025_supplemental.zip
|
@InProceedings{Park_2025_ICCV,
author = {Park, Jinhyung and Romero, Javier and Saito, Shunsuke and Prada, Fabian and Shiratori, Takaaki and Xu, Yichen and Bogo, Federica and Yu, Shoou-I and Kitani, Kris and Khirodkar, Rawal},
title = {ATLAS: Decoupling Skeletal and Shape Parameters for Expressive Parametric Human Modeling},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {6508-6518}
}
|
Parametric body models offer expressive 3D representation of humans across a wide range of poses, shapes, and facial expressions, typically derived by learning a basis over registered 3D meshes. However, existing human mesh modeling approaches struggle to capture detailed variations across diverse body poses and shapes, largely due to limited training data diversity and restrictive modeling assumptions. Moreover, the common paradigm first optimizes the external body surface using a linear basis, then regresses internal skeletal joints from surface vertices. This approach introduces problematic dependencies between internal skeleton and outer soft tissue, limiting direct control over body height and bone lengths. To address these issues, we present ATLAS, a high-fidelity body model learned from 600k high-resolution scans captured using 240 synchronized cameras. Unlike previous methods, we explicitly decouple the shape and skeleton bases by grounding our mesh representation in the human skeleton. This decoupling enables enhanced shape expressivity, fine-grained customization of body attributes, and keypoint fitting independent of external soft-tissue characteristics. ATLAS outperforms existing methods by fitting unseen subjects in diverse poses more accurately, and quantitative evaluations show that our non-linear pose correctives more effectively capture complex poses compared to linear models.
|
2508.15767
|
ICCV 2025; Website: https://jindapark.github.io/projects/atlas/
| null | null |
[] |
[] |
[] |
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] |
385
|
Enhancing Reward Models for High-quality Image Generation: Beyond Text-Image Alignment
|
[
"Ying Ba",
"Tianyu Zhang",
"Yalong Bai",
"Wenyi Mo",
"Tao Liang",
"Bing Su",
"Ji-Rong Wen"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ba_Enhancing_Reward_Models_for_High-quality_Image_Generation_Beyond_Text-Image_Alignment_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ba_Enhancing_Reward_Models_for_High-quality_Image_Generation_Beyond_Text-Image_Alignment_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ba_Enhancing_Reward_Models_ICCV_2025_supplemental.pdf
|
@InProceedings{Ba_2025_ICCV,
author = {Ba, Ying and Zhang, Tianyu and Bai, Yalong and Mo, Wenyi and Liang, Tao and Su, Bing and Wen, Ji-Rong},
title = {Enhancing Reward Models for High-quality Image Generation: Beyond Text-Image Alignment},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {19022-19031}
}
|
Contemporary image generation systems have achieved high fidelity and superior aesthetic quality beyond basic text-image alignment. However, existing evaluation frameworks have failed to evolve in parallel. This study reveals that human preference reward models fine-tuned based on CLIP and BLIP architectures have inherent flaws: they inappropriately assign low scores to images with rich details and high aesthetic value, creating a significant discrepancy with actual human aesthetic preferences. To address this issue, we design a novel evaluation score, ICT (Image-Contained-Text) score, that achieves and surpasses the objectives of text-image alignment by assessing the degree to which images represent textual content. Building upon this foundation, we further train a HP (High-Preference) score model using solely the image modality, aiming to enhance image quality in aspects such as aesthetics and detail refinement while maintaining achieved text-image alignment.Experiments demonstrate that the proposed evaluation model improves scoring accuracy by over 10% compared to existing methods, and achieves significant results in optimizing state-of-the-art text-to-image models. This research provides theoretical foundation and empirical support for the evolution of image generation technology toward better alignment with higher-order human aesthetic preferences.
|
2507.19002
|
Accepted to ICCV 2025
|
https://github.com/BarretBa/ICTHP
| null |
[] |
[
"8y/ICT"
] |
[] |
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] |
386
|
PHATNet: A Physics-guided Haze Transfer Network for Domain-adaptive Real-world Image Dehazing
|
[
"Fu-Jen Tsai",
"Yan-Tsung Peng",
"Yen-Yu Lin",
"Chia-Wen Lin"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Tsai_PHATNet_A_Physics-guided_Haze_Transfer_Network_for_Domain-adaptive_Real-world_Image_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Tsai_PHATNet_A_Physics-guided_Haze_Transfer_Network_for_Domain-adaptive_Real-world_Image_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Tsai_PHATNet_A_Physics-guided_ICCV_2025_supplemental.pdf
|
@InProceedings{Tsai_2025_ICCV,
author = {Tsai, Fu-Jen and Peng, Yan-Tsung and Lin, Yen-Yu and Lin, Chia-Wen},
title = {PHATNet: A Physics-guided Haze Transfer Network for Domain-adaptive Real-world Image Dehazing},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {5591-5600}
}
|
Image dehazing aims to remove unwanted hazy artifacts in images. Although previous research has collected paired real-world hazy and haze-free images to improve dehazing models' performance in real-world scenarios, these models often experience significant performance drops when handling unseen real-world hazy images due to limited training data. This issue motivates us to develop a flexible domain adaptation method to enhance dehazing performance during testing. Observing that predicting haze patterns is generally easier than recovering clean content, we propose the Physics-guided Haze Transfer Network (PHATNet) which transfers haze patterns from unseen target domains to source-domain haze-free images, creating domain-specific fine-tuning sets to update dehazing models for effective domain adaptation. Additionally, we introduce a Haze-Transfer-Consistency loss and a Content-Leakage Loss to enhance PHATNet's disentanglement ability. Experimental results demonstrate that PHATNet significantly boosts state-of-the-art dehazing models on benchmark real-world image dehazing datasets.
|
2507.14826
|
ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
387
|
Diagnosing Pretrained Models for Out-of-distribution Detection
|
[
"Haipeng Xiong",
"Kai Xu",
"Angela Yao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xiong_Diagnosing_Pretrained_Models_for_Out-of-distribution_Detection_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xiong_Diagnosing_Pretrained_Models_for_Out-of-distribution_Detection_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xiong_Diagnosing_Pretrained_Models_ICCV_2025_supplemental.pdf
|
@InProceedings{Xiong_2025_ICCV,
author = {Xiong, Haipeng and Xu, Kai and Yao, Angela},
title = {Diagnosing Pretrained Models for Out-of-distribution Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1836-1845}
}
|
This work questions a common assumption of OOD detection, that models with higher in-distribution (ID) accuracy tend to have better OOD performance. Recent findings show this assumption doesn't always hold. A direct observation is that the later version of torchvision models improves ID accuracy but suffers from a significant drop in OOD performance. We systematically diagnose torchvision training recipes and explain this effect by analyzing the maximal logits of ID and OOD samples. We then propose post-hoc and training-time solutions to mitigate the OOD decrease by fixing problematic augmentations in torchvision recipes. Both solutions enhance OOD detection and maintain strong ID performance.
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388
|
LD-RPS: Zero-Shot Unified Image Restoration via Latent Diffusion Recurrent Posterior Sampling
|
[
"Huaqiu Li",
"Yong Wang",
"Tongwen Huang",
"Hailang Huang",
"Haoqian Wang",
"Xiangxiang Chu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_LD-RPS_Zero-Shot_Unified_Image_Restoration_via_Latent_Diffusion_Recurrent_Posterior_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_LD-RPS_Zero-Shot_Unified_Image_Restoration_via_Latent_Diffusion_Recurrent_Posterior_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_LD-RPS_Zero-Shot_Unified_ICCV_2025_supplemental.pdf
|
@InProceedings{Li_2025_ICCV,
author = {Li, Huaqiu and Wang, Yong and Huang, Tongwen and Huang, Hailang and Wang, Haoqian and Chu, Xiangxiang},
title = {LD-RPS: Zero-Shot Unified Image Restoration via Latent Diffusion Recurrent Posterior Sampling},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13684-13694}
}
|
Unified image restoration is a significantly challenging task in low-level vision. Existing methods either make tailored designs for specific tasks, limiting their generalizability across various types of degradation, or rely on training with paired datasets, thereby suffering from closed-set constraints. To address these issues, we propose a novel, dataset-free, and unified approach through recurrent posterior sampling utilizing a pretrained latent diffusion model. Our method incorporates the multimodal understanding model to provide sematic priors for the generative model under a task-blind condition. Furthermore, it utilizes a lightweight module to align the degraded input with the generated preference of the diffusion model, and employs recurrent refinement for posterior sampling. Extensive experiments demonstrate that our method outperforms state-of-the-art methods, validating its effectiveness and robustness. Our code and data will be available at https://github.com/AMAP-ML/LD-RPS.
| null | null | null | null |
[] |
[] |
[] |
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] |
389
|
Outlier-Aware Post-Training Quantization for Image Super-Resolution
|
[
"Hailing Wang",
"Jianglin Lu",
"Yitian Zhang",
"Yun Fu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Outlier-Aware_Post-Training_Quantization_for_Image_Super-Resolution_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Outlier-Aware_Post-Training_Quantization_for_Image_Super-Resolution_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_Outlier-Aware_Post-Training_Quantization_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Hailing and Lu, Jianglin and Zhang, Yitian and Fu, Yun},
title = {Outlier-Aware Post-Training Quantization for Image Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {16175-16184}
}
|
Quantization techniques, including quantization-aware training (QAT) and post-training quantization (PTQ), have become essential for inference acceleration of image super-resolution (SR) networks. Compared to QAT, PTQ has garnered significant attention as it eliminates the need for ground truth and model retraining. However, existing PTQ methods for SR often fail to achieve satisfactory performance as they overlook the impact of outliers in activation. Our empirical analysis reveals that these prevalent activation outliers are strongly correlated with image color information, and directly removing them leads to significant performance degradation. Motivated by this, we propose a dual-region quantization strategy that partitions activations into an outlier region and a dense region, applying uniform quantization to each region independently to better balance bit-width allocation. Furthermore, we observe that different network layers exhibit varying sensitivities to quantization, leading to different levels of performance degradation. To address this, we introduce sensitivity-aware finetuning that encourages the model to focus more on highly sensitive layers, further enhancing quantization performance. Extensive experiments demonstrate that our method outperforms existing PTQ approaches across various SR networks and datasets, while achieving performance comparable to QAT methods in most scenarios with at least a 75x speedup.
| null | null | null | null |
[] |
[] |
[] |
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] |
390
|
Equipping Vision Foundation Model with Mixture of Experts for Out-of-Distribution Detection
|
[
"Shizhen Zhao",
"Jiahui Liu",
"Xin Wen",
"Haoru Tan",
"Xiaojuan Qi"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhao_Equipping_Vision_Foundation_Model_with_Mixture_of_Experts_for_Out-of-Distribution_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhao_Equipping_Vision_Foundation_Model_with_Mixture_of_Experts_for_Out-of-Distribution_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhao_Equipping_Vision_Foundation_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhao_2025_ICCV,
author = {Zhao, Shizhen and Liu, Jiahui and Wen, Xin and Tan, Haoru and Qi, Xiaojuan},
title = {Equipping Vision Foundation Model with Mixture of Experts for Out-of-Distribution Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1751-1761}
}
|
Pre-trained vision foundation models have transformed many computer vision tasks. Despite their strong ability to learn discriminative and generalizable features crucial for out-of-distribution (OOD) detection, their impact on this task remains underexplored. Motivated by this gap, we systematically investigate representative vision foundation models for OOD detection. Our findings reveal that a pre-trained DINOv2 model, even without fine-tuning on in-domain (ID) data, naturally provides a highly discriminative feature space for OOD detection, achieving performance comparable to existing state-of-the-art methods without requiring complex designs. Beyond this, we explore how fine-tuning foundation models on in-domain (ID) data can enhance OOD detection. However, we observe that the performance of vision foundation models remains unsatisfactory in scenarios with a large semantic space. This is due to the increased complexity of decision boundaries as the number of categories grows, which complicates the optimization process. To mitigate this, we propose the Mixture of Feature Experts (MoFE) module, which partitions features into subspaces, effectively capturing complex data distributions and refining decision boundaries. Further, we introduce a Dynamic-b Mixup strategy, which samples interpolation weights from a dynamic beta distribution. This adapts to varying levels of learning difficulty across categories, improving feature learning for more challenging categories. Extensive experiments demonstrate the effectiveness of our approach, significantly outperforming baseline methods.
|
2510.10584
| null | null | null |
[] |
[] |
[] |
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] |
391
|
The Curse of Conditions: Analyzing and Improving Optimal Transport for Conditional Flow-Based Generation
|
[
"Ho Kei Cheng",
"Alexander Schwing"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Cheng_The_Curse_of_Conditions_Analyzing_and_Improving_Optimal_Transport_for_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Cheng_The_Curse_of_Conditions_Analyzing_and_Improving_Optimal_Transport_for_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Cheng_The_Curse_of_ICCV_2025_supplemental.pdf
|
@InProceedings{Cheng_2025_ICCV,
author = {Cheng, Ho Kei and Schwing, Alexander},
title = {The Curse of Conditions: Analyzing and Improving Optimal Transport for Conditional Flow-Based Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {15875-15884}
}
|
Minibatch optimal transport coupling straightens paths in unconditional flow matching. This leads to computationally less demanding inference as fewer integration steps and less complex numerical solvers can be employed when numerically solving an ordinary differential equation at test time. However, in the conditional setting, minibatch optimal transport falls short. This is because the default optimal transport mapping disregards conditions, resulting in a conditionally skewed prior distribution during training. In contrast, at test time, we have no access to the skewed prior, and instead sample from the full, unbiased prior distribution. This gap between training and testing leads to a subpar performance. To bridge this gap, we propose conditional optimal transport (C2OT) that adds a conditional weighting term in the cost matrix when computing the optimal transport assignment. Experiments demonstrate that this simple fix works with both discrete and continuous conditions in 8gaussians-to-moons, CIFAR-10, ImageNet-32x32, and ImageNet-256x256. Our method performs better overall compared to the existing baselines across different function evaluation budgets. Code is available at hkchengrex.github.io/C2OT
|
2503.10636
|
ICCV 2025. Project page: https://hkchengrex.github.io/C2OT
| null |
https://hkchengrex.com/C2OT/
|
[] |
[
"hkchengrex/c2ot-cifar10-fm"
] |
[] |
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] |
392
|
BlinkTrack: Feature Tracking over 80 FPS via Events and Images
|
[
"Yichen Shen",
"Yijin Li",
"Shuo Chen",
"Guanglin Li",
"Zhaoyang Huang",
"Hujun Bao",
"Zhaopeng Cui",
"Guofeng Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Shen_BlinkTrack_Feature_Tracking_over_80_FPS_via_Events_and_Images_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Shen_BlinkTrack_Feature_Tracking_over_80_FPS_via_Events_and_Images_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Shen_BlinkTrack_Feature_Tracking_ICCV_2025_supplemental.zip
|
@InProceedings{Shen_2025_ICCV,
author = {Shen, Yichen and Li, Yijin and Chen, Shuo and Li, Guanglin and Huang, Zhaoyang and Bao, Hujun and Cui, Zhaopeng and Zhang, Guofeng},
title = {BlinkTrack: Feature Tracking over 80 FPS via Events and Images},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9298-9308}
}
|
Event cameras, known for their high temporal resolution and ability to capture asynchronous changes, have gained significant attention for their potential in feature tracking, especially in challenging conditions. However, event cameras lack the fine-grained texture information that conventional cameras provide, leading to error accumulation in tracking. To address this, we propose a novel framework, BlinkTrack, which integrates event data with grayscale images for high-frequency feature tracking. Our method extends the traditional Kalman filter into a learning-based framework, utilizing differentiable Kalman filters in both event and image branches. This approach improves single-modality tracking and effectively solves the data association and fusion from asynchronous event and image data. We also introduce new synthetic and augmented datasets to better evaluate our model. Experimental results indicate that BlinkTrack significantly outperforms existing methods, exceeding 80 FPS with multi-modality data and 100 FPS with preprocessed event data. Codes and dataset are available at https://github.com/ColieShen/BlinkTrack.
|
2409.17981
| null |
https://github.com/ColieShen/BlinkTrack
| null |
[] |
[] |
[] |
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393
|
Less is More: Improving Motion Diffusion Models with Sparse Keyframes
|
[
"Jinseok Bae",
"Inwoo Hwang",
"Young-Yoon Lee",
"Ziyu Guo",
"Joseph Liu",
"Yizhak Ben-Shabat",
"Young Min Kim",
"Mubbasir Kapadia"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Bae_Less_is_More_Improving_Motion_Diffusion_Models_with_Sparse_Keyframes_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Bae_Less_is_More_Improving_Motion_Diffusion_Models_with_Sparse_Keyframes_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Bae_Less_is_More_ICCV_2025_supplemental.zip
|
@InProceedings{Bae_2025_ICCV,
author = {Bae, Jinseok and Hwang, Inwoo and Lee, Young-Yoon and Guo, Ziyu and Liu, Joseph and Ben-Shabat, Yizhak and Kim, Young Min and Kapadia, Mubbasir},
title = {Less is More: Improving Motion Diffusion Models with Sparse Keyframes},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11069-11078}
}
|
Recent advances in motion diffusion models have led to remarkable progress in diverse motion generation tasks, including text-to-motion synthesis.However, existing approaches represent motions as dense frame sequences, requiring the model to process redundant or less informative frames.The processing of dense animation frames imposes significant training complexity, especially when learning intricate distributions of large motion datasets even with modern neural architectures. This severely limits the performance of generative motion models for downstream tasks.Inspired by professional animators who mainly focus on sparse keyframes, we propose a novel diffusion framework explicitly designed around sparse and geometrically meaningful keyframes.Our method reduces computation by masking non-keyframes and efficiently interpolating missing frames. We dynamically refine the keyframe mask during inference to prioritize informative frames in later diffusion steps.Extensive experiments show that our approach consistently outperforms state-of-the-art methods in text alignment and motion realism, while also effectively maintaining high performance at significantly fewer diffusion steps.We further validate the robustness of our framework by using it as a generative prior and adapting it to different downstream tasks.
|
2503.13859
| null | null | null |
[] |
[] |
[] |
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] |
394
|
CAVIS: Context-Aware Video Instance Segmentation
|
[
"Seunghun Lee",
"Jiwan Seo",
"Kiljoon Han",
"Minwoo Choi",
"Sunghoon Im"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Lee_CAVIS_Context-Aware_Video_Instance_Segmentation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Lee_CAVIS_Context-Aware_Video_Instance_Segmentation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Lee_CAVIS_Context-Aware_Video_ICCV_2025_supplemental.pdf
|
@InProceedings{Lee_2025_ICCV,
author = {Lee, Seunghun and Seo, Jiwan and Han, Kiljoon and Choi, Minwoo and Im, Sunghoon},
title = {CAVIS: Context-Aware Video Instance Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4507-4517}
}
|
In this paper, we introduce the Context-Aware Video Instance Segmentation (CAVIS), a novel framework designed to enhance instance association by integrating contextual information adjacent to each object. To efficiently extract and leverage this information, we propose the Context-Aware Instance Tracker (CAIT), which merges contextual data surrounding the instances with the core instance features to improve tracking accuracy. Additionally, we design the Prototypical Cross-frame Contrastive (PCC) loss, which ensures consistency in object-level features across frames, thereby significantly enhancing instance matching accuracy. CAVIS demonstrates superior performance over state-of-the-art methods on all benchmark datasets in video instance segmentation (VIS) and video panoptic segmentation (VPS). Notably, our method excels on the OVIS dataset, which is known for its particularly challenging videos.
|
2407.03010
|
ICCV 2025. Code: https://github.com/Seung-Hun-Lee/CAVIS
| null |
https://seung-hun-lee.github.io/projects/CAVIS/
|
[] |
[
"DGIST-CVLAB-Video/CAVIS"
] |
[] |
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] |
395
|
LeanVAE: An Ultra-Efficient Reconstruction VAE for Video Diffusion Models
|
[
"Yu Cheng",
"Fajie Yuan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Cheng_LeanVAE_An_Ultra-Efficient_Reconstruction_VAE_for_Video_Diffusion_Models_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Cheng_LeanVAE_An_Ultra-Efficient_Reconstruction_VAE_for_Video_Diffusion_Models_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Cheng_LeanVAE_An_Ultra-Efficient_ICCV_2025_supplemental.pdf
|
@InProceedings{Cheng_2025_ICCV,
author = {Cheng, Yu and Yuan, Fajie},
title = {LeanVAE: An Ultra-Efficient Reconstruction VAE for Video Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {15692-15702}
}
|
Recent advances in Latent Video Diffusion Models (LVDMs) have revolutionized video generation by leveraging Video Variational Autoencoders (Video VAEs) to compress intricate video data into a compact latent space. However, as LVDM training scales, the computational overhead of Video VAEs becomes a critical bottleneck, particularly for encoding high-resolution videos. To address this, we propose LeanVAE, a novel and ultra-efficient Video VAE framework that introduce two key innovations: (1) a lightweight architecture based on a Neighborhood-Aware Feedforward (NAF) module and non-overlapping patch operations, drastically reducing computational cost, and (2) the integration of wavelet transforms and compressed sensing techniques to enhance reconstruction quality. Extensive experiments validate LeanVAE's superiority in video reconstruction and generation, particularly in enhancing efficiency over existing Video VAEs. Our model offers up to 50x fewer FLOPs and 44x faster inference speed while maintaining competitive reconstruction quality, providing insights for scalable, efficient video generation. Our models and code are available at https://github.com/westlake-repl/LeanVAE.
|
2503.14325
| null |
https://github.com/westlake-repl/LeanVAE
| null |
[] |
[
"Yumic/LeanVAE"
] |
[] |
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] |
396
|
Monocular Semantic Scene Completion via Masked Recurrent Networks
|
[
"Xuzhi Wang",
"Xinran Wu",
"Song Wang",
"Lingdong Kong",
"Ziping Zhao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Monocular_Semantic_Scene_Completion_via_Masked_Recurrent_Networks_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Monocular_Semantic_Scene_Completion_via_Masked_Recurrent_Networks_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_Monocular_Semantic_Scene_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Xuzhi and Wu, Xinran and Wang, Song and Kong, Lingdong and Zhao, Ziping},
title = {Monocular Semantic Scene Completion via Masked Recurrent Networks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24811-24822}
}
|
Monocular Semantic Scene Completion (MSSC) aims to predict the voxel-wise occupancy and semantic category from a single-view RGB image. Existing methods adopt a single-stage framework that aims to simultaneously achieve visible region segmentation and occluded region hallucination, while also being affected by inaccurate depth estimation. Such methods often achieve suboptimal performance, especially in complex scenes. We propose a novel two-stage framework that decomposes MSSC into coarse MSSC followed by the Masked Recurrent Network. Specifically, we propose the Masked Sparse Gated Recurrent Unit (MS-GRU) which concentrates on the occupied regions by the proposed mask updating mechanism, and a sparse GRU design is proposed to reduce the computation cost. Additionally, we propose the distance attention projection to reduce projection errors by assigning different attention scores according to the distance to the observed surface. Experimental results demonstrate that our proposed unified framework, MonoMRN, effectively supports both indoor and outdoor scenes and achieves state-of-the-art performance on the NYUv2 and SemanticKITTI datasets. Furthermore, we conduct robustness analysis under various disturbances, highlighting the role of the Masked Recurrent Network in enhancing the model's resilience to such challenges. The source code is publicly available at: https://github.com/alanWXZ/MonoMRN.
|
2507.17661
|
ICCV 2025; 15 pages, 10 figures, 6 tables; Code at
https://github.com/alanWXZ/MonoMRN
| null | null |
[] |
[] |
[] |
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] |
397
|
TurboReg: TurboClique for Robust and Efficient Point Cloud Registration
|
[
"Shaocheng Yan",
"Pengcheng Shi",
"Zhenjun Zhao",
"Kaixin Wang",
"Kuang Cao",
"Ji Wu",
"Jiayuan Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yan_TurboReg_TurboClique_for_Robust_and_Efficient_Point_Cloud_Registration_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yan_TurboReg_TurboClique_for_Robust_and_Efficient_Point_Cloud_Registration_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yan_TurboReg_TurboClique_for_ICCV_2025_supplemental.pdf
|
@InProceedings{Yan_2025_ICCV,
author = {Yan, Shaocheng and Shi, Pengcheng and Zhao, Zhenjun and Wang, Kaixin and Cao, Kuang and Wu, Ji and Li, Jiayuan},
title = {TurboReg: TurboClique for Robust and Efficient Point Cloud Registration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26371-26381}
}
|
Robust estimation is essential in correspondence-based Point Cloud Registration (PCR). Existing methods using maximal clique search in compatibility graphs achieve high recall but suffer from exponential time complexity, limiting their use in time-sensitive applications. To address this challenge, we propose a fast and robust estimator, TurboReg, built upon a novel lightweight clique, TurboClique, and a highly parallelizable Pivot-Guided Search (PGS) algorithm. First, we define the TurboClique as a 3-clique within a highly-constrained compatibility graph. The lightweight nature of the 3-clique allows for efficient parallel searching, and the highly-constrained compatibility graph ensures robust spatial consistency for stable transformation estimation. Next, PGS selects matching pairs with high SC^2 scores as pivots, effectively guiding the search toward TurboCliques with higher inlier ratios. Moreover, the PGS algorithm has linear time complexity and is significantly more efficient than the maximal clique search with exponential time complexity. Extensive experiments show that TurboReg achieves state-of-the-art performance across multiple real-world datasets, with substantial speed improvements. For example, on the 3DMatch+FCGF dataset, TurboReg (1K) operates 208.22xfaster than 3DMAC while also achieving higher recall. Our code is accessible at \href https://github.com/Laka-3DV/TurboReg \texttt TurboReg .
|
2507.01439
|
ICCV-2025 Accepted Paper
|
https://github.com/Laka-3DV/TurboReg
| null |
[] |
[] |
[] |
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] |
398
|
AR-VRM: Imitating Human Motions for Visual Robot Manipulation with Analogical Reasoning
|
[
"Dejie Yang",
"Zijing Zhao",
"Yang Liu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yang_AR-VRM_Imitating_Human_Motions_for_Visual_Robot_Manipulation_with_Analogical_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yang_AR-VRM_Imitating_Human_Motions_for_Visual_Robot_Manipulation_with_Analogical_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yang_AR-VRM_Imitating_Human_ICCV_2025_supplemental.pdf
|
@InProceedings{Yang_2025_ICCV,
author = {Yang, Dejie and Zhao, Zijing and Liu, Yang},
title = {AR-VRM: Imitating Human Motions for Visual Robot Manipulation with Analogical Reasoning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {6818-6827}
}
|
Visual Robot Manipulation (VRM) aims to enable a robot to follow natural language instructions based on robot states and visual observations, and therefore requires costly multi- modal data. To compensate for the deficiency of robot data, existing approaches have employed vision-language pre- training with large-scale data. However, they either utilize web data that differs from robotic tasks, or train the model in an implicit way (e.g., predicting future frames at the pixel level), thus showing limited generalization ability under in- sufficient robot data. In this paper, we propose to learn from large-scale human action video datasets in an explicit way (i.e., imitating human actions from hand keypoints), introduc- ing Visual Robot Manipulation with Analogical Reasoning (AR-VRM). To acquire action knowledge explicitly from hu- man action videos, we propose a keypoint Vision-Language Model (VLM) pretraining scheme, enabling the VLM to learn human action knowledge and directly predict human hand keypoints. During fine-tuning on robot data, to facilitate the robotic arm in imitating the action patterns of human motions, we first retrieve human action videos that perform similar manipulation tasks and have similar historical obser- vations, and then learn the Analogical Reasoning (AR) map between human hand keypoints and robot components. Tak- ing advantage of focusing on action keypoints instead of irrel- evant visual cues, our method achieves leading performance on the CALVIN benchmark and real-world experiments. In few-shot scenarios, our AR-VRM outperforms previous meth- ods by large margins, underscoring the effectiveness of explicitly imitating human actions under data scarcity. Code available at https://github.com/idejie/ar.
| null | null | null | null |
[] |
[] |
[] |
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] |
399
|
Self-Calibrated Variance-Stabilizing Transformations for Real-World Image Denoising
|
[
"Sébastien Herbreteau",
"Michael Unser"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Herbreteau_Self-Calibrated_Variance-Stabilizing_Transformations_for_Real-World_Image_Denoising_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Herbreteau_Self-Calibrated_Variance-Stabilizing_Transformations_for_Real-World_Image_Denoising_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Herbreteau_Self-Calibrated_Variance-Stabilizing_Transformations_ICCV_2025_supplemental.pdf
|
@InProceedings{Herbreteau_2025_ICCV,
author = {Herbreteau, S\'ebastien and Unser, Michael},
title = {Self-Calibrated Variance-Stabilizing Transformations for Real-World Image Denoising},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10496-10506}
}
|
Supervised deep learning has become the method of choice for image denoising. It involves the training of neural networks on large datasets composed of pairs of noisy and clean images. However, the necessity of training data that are specific to the targeted application constrains the widespread use of denoising networks. Recently, several approaches have been developed to overcome this difficulty by whether artificially generating realistic clean/noisy image pairs, or training exclusively on noisy images. In this paper, we show that, contrary to popular belief, denoising networks specialized in the removal of Gaussian noise can be efficiently leveraged in favor of real-world image denoising, even without additional training. For this to happen, an appropriate variance-stabilizing transform (VST) has to be applied beforehand. We propose an algorithm termed Noise2VST for the learning of such a model-free VST. Our approach requires only the input noisy image and an off-the-shelf Gaussian denoiser. We demonstrate through extensive experiments the efficiency and superiority of Noise2VST in comparison to existing methods trained in the absence of specific clean/noisy pairs.
|
2407.17399
|
Accepted at IEEE/CVF International Conference on Computer Vision
(ICCV) 2025
| null | null |
[] |
[] |
[] |
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