# papers

title: Sonata: Self-Supervised Learning of Reliable Point Representations
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22193-22204
affiliations: ["The University of Hong Kong", "Meta Reality Labs Research"]
cities: ["Hong Kong", "Menlo Park"]
countries: ["China", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Sonata_Self-Supervised_Learning_of_Reliable_Point_Representations_CVPR_2025_paper.html

authors:
Xiaoyang Wu, Daniel DeTone, Duncan Frost, Tianwei Shen, Chris Xie, Nan Yang, Jakob Engel, Richard Newcombe, Hengshuang Zhao, Julian Straub

abstract:
In this paper, we question whether we have a reliable self-supervised point cloud model that can be used for diverse 3D tasks via simple linear probing, even with limited data and minimal computation. We find that existing 3D self-supervised learning approaches fall short when evaluated on representation quality through linear probing. We hypothesize that this is due to what we term the "geometric shortcut", which causes representations to collapse to low-level spatial features. This challenge is unique to 3D and arises from the sparse nature of point cloud data. We address it through two key strategies: obscuring spatial information and enhancing the reliance on input features, ultimately composing a Sonata of 140k point clouds through self-distillation. Sonata is simple and intuitive, yet its learned representations are strong and reliable: zero-shot visualizations demonstrate semantic grouping, alongside strong spatial reasoning through nearest-neighbor relationships. Sonata demonstrates exceptional parameter and data efficiency, tripling linear probing accuracy (from 21.8% to 72.5%) on ScanNet and nearly doubling performance with only 1% of the data compared to previous approaches. Full fine-tuning further advances SOTA across both 3D indoor and outdoor perception tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Sonata_Self-Supervised_Learning_of_Reliable_Point_Representations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Sonata_Self-Supervised_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Xiaoyang and DeTone, Daniel and Frost, Duncan and Shen, Tianwei and Xie, Chris and Yang, Nan and Engel, Jakob and Newcombe, Richard and Zhao, Hengshuang and Straub, Julian},
    title     = {Sonata: Self-Supervised Learning of Reliable Point Representations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22193-22204}
}

raw_affiliation:
[{"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Meta Reality Labs Research", "city": "Menlo Park", "country": "USA"}]


authors: Hongbin Lin, Zilu Guo, Yifan Zhang, Shuaicheng Niu, Yafeng Li, Ruimao Zhang, Shuguang Cui, Zhen Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27497-27507
affiliations: ["Computer Vision Foundation", "IEEE"]
cities: ["Los Alamitos", "New York"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_DriveGEN_Generalized_and_Robust_3D_Detection_in_Driving_via_Controllable_CVPR_2025_paper.html

title:
DriveGEN: Generalized and Robust 3D Detection in Driving via Controllable Text-to-Image Diffusion Generation

abstract:
In autonomous driving, vision-centric 3D detection aims to identify 3D objects from images. However, high data collection costs and diverse real-world scenarios limit the scale of training data. Once distribution shifts occur between training and test data, existing methods often suffer from performance degradation, known as Out-of-Distribution (OOD) problems. To address this, controllable Text-to-Image (T2I) diffusion offers a potential solution for training data enhancement, which is required to generate diverse OOD scenarios with precise 3D object geometry. Nevertheless, existing controllable T2I approaches are restricted by the limited scale of training data or struggle to preserve all annotated 3D objects. In this paper, we present DriveGEN, a method designed to improve the robustness of 3D detectors in Driving via Training-Free Controllable Text-to-Image Diffusion Generation. Without extra diffusion model training, DriveGEN consistently preserves objects with precise 3D geometry across diverse OOD generations, consisting of 2 stages: 1) Self-Prototype Extraction: We empirically find that self-attention features are semantic-aware but require accurate region selection for 3D objects. Thus, we extract precise object features via layouts to capture 3D object geometry, termed self-prototypes. 2) Prototype-Guided Diffusion: To preserve objects across various OOD scenarios, we perform semantic-aware feature alignment and shallow feature alignment during denoising. Extensive experiments demonstrate the effectiveness of DriveGEN in improving 3D detection. The code is available at github.com/Hongbin98/DriveGEN.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_DriveGEN_Generalized_and_Robust_3D_Detection_in_Driving_via_Controllable_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_DriveGEN_Generalized_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Hongbin and Guo, Zilu and Zhang, Yifan and Niu, Shuaicheng and Li, Yafeng and Zhang, Ruimao and Cui, Shuguang and Li, Zhen},
    title     = {DriveGEN: Generalized and Robust 3D Detection in Driving via Controllable Text-to-Image Diffusion Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27497-27507}
}

raw_affiliation:
[{"affiliation": "Computer Vision Foundation", "city": "Los Alamitos", "country": "USA"}, {"affiliation": "IEEE", "city": "New York", "country": "USA"}]


title: GauSTAR: Gaussian Surface Tracking and Reconstruction
authors: Chengwei Zheng, Lixin Xue, Juan Zarate, Jie Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16543-16553
affiliations: ["ETH Z \u00a8urich", "HKUST(GZ)", "HKUST"]
cities: ["Zurich", "Guangzhou", "Hong Kong"]
countries: ["Switzerland", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_GauSTAR_Gaussian_Surface_Tracking_and_Reconstruction_CVPR_2025_paper.html

abstract:
3D Gaussian Splatting techniques have enabled efficient photo-realistic rendering of static scenes. Recent works have extended these approaches to support surface reconstruction and tracking. However, tracking dynamic surfaces with 3D Gaussians remains challenging due to complex topology changes, such as surfaces appearing, disappearing, or splitting. To address these challenges, we propose GauSTAR, a novel method that achieves photo-realistic rendering, accurate surface reconstruction, and reliable 3D tracking for general dynamic scenes with changing topology. Given multi-view captures as input, GauSTAR binds Gaussians to mesh faces to represent dynamic objects. For surfaces with consistent topology, GauSTAR maintains the mesh topology and tracks the meshes using Gaussians. For regions where topology changes, GauSTAR adaptively unbinds Gaussians from the mesh, enabling accurate registration and generation of new surfaces based on these optimized Gaussians. Additionally, we introduce a surface-based scene flow method that provides robust initialization for tracking between frames. Experiments demonstrate that our method effectively tracks and reconstructs dynamic surfaces, enabling a range of applications. Our project page with the code release is available at https://eth-ait.github.io/GauSTAR/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_GauSTAR_Gaussian_Surface_Tracking_and_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_GauSTAR_Gaussian_Surface_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zheng_2025_CVPR,
    author    = {Zheng, Chengwei and Xue, Lixin and Zarate, Juan and Song, Jie},
    title     = {GauSTAR: Gaussian Surface Tracking and Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16543-16553}
}

raw_affiliation:
[{"affiliation": "ETH Z \u00a8urich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "HKUST(GZ)", "city": "Guangzhou", "country": "China"}, {"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}]


title: Training-free Dense-Aligned Diffusion Guidance for Modular Conditional Image Synthesis
authors: Zixuan Wang, Duo Peng, Feng Chen, Yuwei Yang, Yinjie Lei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13135-13145
cities: ["Chengdu", "Singapore", "Adelaide", "Canberra"]
countries: ["China", "Singapore", "Australia", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Training-free_Dense-Aligned_Diffusion_Guidance_for_Modular_Conditional_Image_Synthesis_CVPR_2025_paper.html

abstract:
Conditional image synthesis is a crucial task with broad applications, such as artistic creation and virtual reality. However, current generative methods are often task-oriented with a narrow scope, handling a restricted condition with constrained applicability. In this paper, we propose a novel approach that treats conditional image synthesis as the modular combination of diverse fundamental condition units. Specifically, we divide conditions into three primary units: text, layout, and drag. To enable effective control over these conditions, we design a dedicated alignment module for each. For the text condition, we introduce a Dense Concept Alignment (DCA) module, which achieves dense visual-text alignment by drawing on diverse textual concepts. For the layout condition, we propose a Dense Geometry Alignment (DGA) module to enforce comprehensive geometric constraints that preserve the spatial configuration. For the drag condition, we introduce a Dense Motion Alignment (DMA) module to apply multi-level motion regularization, ensuring that each pixel follows its desired trajectory without visual artifacts. By flexibly inserting and combining these alignment modules, our framework enhances the model's adaptability to diverse conditional generation tasks and greatly expands its application range. Extensive experiments demonstrate the superior performance of our framework across a variety of conditions, including textual description, segmentation mask (bounding box), drag manipulation, and their combinations. Code is available at  https://github.com/ZixuanWang0525/DADG

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Training-free_Dense-Aligned_Diffusion_Guidance_for_Modular_Conditional_Image_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Training-free_Dense-Aligned_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zixuan and Peng, Duo and Chen, Feng and Yang, Yuwei and Lei, Yinjie},
    title     = {Training-free Dense-Aligned Diffusion Guidance for Modular Conditional Image Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13135-13145}
}

raw_affiliation:
[{"affiliation": "Sichuan University", "city": "Chengdu", "country": "China"}, {"affiliation": "Singapore University of Technology and Design", "city": "Singapore", "country": "Singapore"}, {"affiliation": "The University of Adelaide", "city": "Adelaide", "country": "Australia"}, {"affiliation": "Australian National University", "city": "Canberra", "country": "Australia"}]

affiliations:
["Sichuan University", "Singapore University of Technology and Design", "The University of Adelaide", "Australian National University"]


title: DRiVE: Diffusion-based Rigging Empowers Generation of Versatile and Expressive Characters
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21170-21180
cities: ["Shenzhen", "Shanghai", "Shenzhen", "Hong Kong", "Hong Kong"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_DRiVE_Diffusion-based_Rigging_Empowers_Generation_of_Versatile_and_Expressive_Characters_CVPR_2025_paper.html

authors:
Mingze Sun, Junhao Chen, Junting Dong, Yurun Chen, Xinyu Jiang, Shiwei Mao, Puhua Jiang, Jingbo Wang, Bo Dai, Ruqi Huang

abstract:
Recent advances in generative models have enabled high-quality 3D character reconstruction from multi-modal. However, animating these generated characters remains a challenging task, especially for complex elements like garments and hair, due to the lack of large-scale datasets and effective rigging methods. To address this gap, we curate AnimeRig, a large-scale dataset with detailed skeleton and skinning annotations. Building upon this, we propose DRiVE, a novel framework for generating and rigging 3D human characters with intricate structures. Unlike existing methods, DRiVE utilizes a 3D Gaussian representation, facilitating efficient animation and high-quality rendering. We further introduce GSDiff, a 3D Gaussian-based diffusion module that predicts joint positions as spatial distributions, overcoming the limitations of regression-based approaches. Extensive experiments demonstrate that DRiVE achieves precise rigging results, enabling realistic dynamics for clothing and hair, and surpassing previous methods in both quality and versatility. Code, dataset and visualization results are available at https://DRiVEAvatar.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_DRiVE_Diffusion-based_Rigging_Empowers_Generation_of_Versatile_and_Expressive_Characters_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_DRiVE_Diffusion-based_Rigging_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Mingze and Chen, Junhao and Dong, Junting and Chen, Yurun and Jiang, Xinyu and Mao, Shiwei and Jiang, Puhua and Wang, Jingbo and Dai, Bo and Huang, Ruqi},
    title     = {DRiVE: Diffusion-based Rigging Empowers Generation of Versatile and Expressive Characters},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21170-21180}
}

raw_affiliation:
[{"affiliation": "Tsinghua Shenzhen International Graduate School", "city": "Shenzhen", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "PengCheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Feeling AI", "city": "Hong Kong", "country": "China"}]

affiliations:
["Tsinghua Shenzhen International Graduate School", "Shanghai AI Laboratory", "PengCheng Laboratory", "The University of Hong Kong", "Feeling AI"]


title: Online Video Understanding: OVBench and VideoChat-Online
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3328-3338
cities: ["Nanjing", "Beijing", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Online_Video_Understanding_OVBench_and_VideoChat-Online_CVPR_2025_paper.html

authors:
Zhenpeng Huang, Xinhao Li, Jiaqi Li, Jing Wang, Xiangyu Zeng, Cheng Liang, Tao Wu, Xi Chen, Liang Li, Limin Wang

abstract:
Multimodal Large Language Models (MLLMs) have significantly progressed in offline video understanding. However, applying these models to real-world scenarios, such as autonomous driving and human-computer interaction, presents unique challenges due to the need for real-time processing of continuous online video streams. To this end, this paper presents systematic efforts from three perspectives: evaluation benchmark, model architecture, and training strategy. First, we introduce OVBench, a comprehensive question-answering benchmark designed to evaluate models' ability to perceive, memorize, and reason within online video contexts. It features 6 core task types across three temporal contexts--past, current, and future--forming 16 subtasks from diverse datasets. Second, we propose a new Pyramid Memory Bank (PMB) that effectively retains key spatiotemporal information in video streams. Third, we proposed an offline-to-online learning paradigm, designing an interleaved dialogue format for online video data and constructing an instruction-tuning dataset tailored for online video training. This framework led to the development of VideoChat-Online, a robust and efficient model for online video understanding. Despite the lower computational cost and higher efficiency, VideoChat-Online outperforms existing state-of-the-art offline and online models across popular offline video benchmarks and OVBench, demonstrating the effectiveness of our model architecture and training strategy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Online_Video_Understanding_OVBench_and_VideoChat-Online_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Online_Video_Understanding_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Zhenpeng and Li, Xinhao and Li, Jiaqi and Wang, Jing and Zeng, Xiangyu and Liang, Cheng and Wu, Tao and Chen, Xi and Li, Liang and Wang, Limin},
    title     = {Online Video Understanding: OVBench and VideoChat-Online},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3328-3338}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory for Novel Software Technology, Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "China Mobile Research Institute", "city": "Beijing", "country": "China"}, {"affiliation": "OpenGVLab, Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}]

affiliations:
["State Key Laboratory for Novel Software Technology, Nanjing University", "China Mobile Research Institute", "OpenGVLab, Shanghai AI Laboratory"]


title: TADFormer: Task-Adaptive Dynamic TransFormer for Efficient Multi-Task Learning
authors: Seungmin Baek, Soyul Lee, Hayeon Jo, Hyesong Choi, Dongbo Min
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14858-14868
raw_affiliation: [{"affiliation": "Ewha Womans University", "city": "Seoul", "country": "South Korea"}]
affiliations: ["Ewha Womans University"]
cities: ["Seoul"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Baek_TADFormer_Task-Adaptive_Dynamic_TransFormer_for_Efficient_Multi-Task_Learning_CVPR_2025_paper.html

abstract:
Transfer learning paradigm has driven substantial advancements in various vision tasks. However, as state-of-the-art models continue to grow, classical full fine-tuning often becomes computationally impractical, particularly in multi-task learning (MTL) setup where training complexity increases proportional to the number of tasks. Consequently, recent studies have explored Parameter-Efficient Fine-Tuning (PEFT) for MTL architectures. Despite some progress, these approaches still exhibit limitations in capturing fine-grained, task-specific features that are crucial to MTL. In this paper, we introduce Task-Adaptive Dynamic transFormer, termed TADFormer, a novel PEFT framework that performs task-aware feature adaptation in the fine-grained manner by dynamically considering task-specific input contexts. TADFormer proposes the parameter-efficient prompting for task adaptation and the Dynamic Task Filter (DTF) to capture task information conditioned on input contexts. Experiments on the PASCAL-Context benchmark demonstrate that the proposed method achieves higher accuracy in dense scene understanding tasks, while reducing the number of trainable parameters by up to 8.4 times when compared to full fine-tuning of MTL models. TADFormer also demonstrates superior parameter efficiency and accuracy compared to recent PEFT methods. Our code is available at supplementary material.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Baek_TADFormer_Task-Adaptive_Dynamic_TransFormer_for_Efficient_Multi-Task_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Baek_TADFormer_Task-Adaptive_Dynamic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Baek_2025_CVPR,
    author    = {Baek, Seungmin and Lee, Soyul and Jo, Hayeon and Choi, Hyesong and Min, Dongbo},
    title     = {TADFormer: Task-Adaptive Dynamic TransFormer for Efficient Multi-Task Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14858-14868}
}


authors: Qiang Li, Jian Ruan, Fanghao Wu, Yuchi Chen, Zhihua Wei, Wen Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27315-27324
raw_affiliation: [{"affiliation": "Tongji University", "city": "Shanghai", "country": "China"}]
affiliations: ["Tongji University"]
cities: ["Shanghai"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_A_Unified_Approach_to_Interpreting_Self-supervised_Pre-training_Methods_for_3D_CVPR_2025_paper.html

title:
A Unified Approach to Interpreting Self-supervised Pre-training Methods for 3D Point Clouds via Interactions

abstract:
Recently, many self-supervised pre-training methods have been proposed to improve the performance of deep neural networks (DNNs) for 3D point clouds processing. However, the common mechanism underlying the effectiveness of different pre-training methods remains unclear. In this paper, we use game-theoretic interactions as a unified approach to explore the common mechanism of pre-training methods. Specifically, we decompose the output score of a DNN into the sum of numerous effects of interactions, with each interaction representing a distinct 3D substructure of the input point cloud. Based on the decomposed interactions, we draw the following conclusions. (1) The common mechanism across different pre-training methods is that they enhance the strength of high-order interactions encoded by DNNs, which represent complex and global 3D structures, while reducing the strength of low-order interactions, which represent simple and local 3D structures. (2) Sufficient pre-training and adequate fine-tuning data for downstream tasks further reinforce the mechanism described above. (3) Pre-training methods carry a potential risk of reducing the transferability of features encoded by DNNs. Inspired by the observed common mechanism, we propose a new method to directly enhance the strength of high-order interactions and reduce the strength of low-order interactions encoded by DNNs, improving performance without the need for pre-training on large-scale datasets. Experiments show that our method achieves performance comparable to traditional pre-training methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_A_Unified_Approach_to_Interpreting_Self-supervised_Pre-training_Methods_for_3D_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_A_Unified_Approach_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Qiang and Ruan, Jian and Wu, Fanghao and Chen, Yuchi and Wei, Zhihua and Shen, Wen},
    title     = {A Unified Approach to Interpreting Self-supervised Pre-training Methods for 3D Point Clouds via Interactions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27315-27324}
}


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20990-21000
cities: ["Chicago", "Stony Brook", "Stony Brook"]
countries: ["USA", "USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Konwer_Enhancing_SAM_with_Efficient_Prompting_and_Preference_Optimization_for_Semi-supervised_CVPR_2025_paper.html

title:
Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation

authors:
Aishik Konwer, Zhijian Yang, Erhan Bas, Cao Xiao, Prateek Prasanna, Parminder Bhatia, Taha Kass-Hout

abstract:
Foundational models such as the Segment Anything Model (SAM) are gaining traction in medical imaging segmentation, supporting multiple downstream tasks. However, such models are supervised in nature, still relying on large annotated datasets or prompts supplied by experts. Conventional techniques such as active learning to alleviate such limitations are limited in scope and still necessitate continuous human involvement and complex domain knowledge for label refinement or establishing reward ground truth. To address these challenges, we propose an enhanced Segment Anything Model (SAM) framework that utilizes annotation-efficient prompts generated in a fully unsupervised fashion, while still capturing essential semantic, location, and shape information through contrastive language-image pretraining and visual question answering. We adopt the direct preference optimization technique to design an optimal policy that enables the model to generate high-fidelity segmentations with simple ratings or rankings provided by a virtual annotator simulating the human annotation process. State-of-the-art performance of our framework in tasks such as lung segmentation, breast tumor segmentation, and organ segmentation across various modalities, including X-ray, ultrasound, and abdominal CT, justifies its effectiveness in low-annotation data scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Konwer_Enhancing_SAM_with_Efficient_Prompting_and_Preference_Optimization_for_Semi-supervised_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Konwer_Enhancing_SAM_with_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Konwer_2025_CVPR,
    author    = {Konwer, Aishik and Yang, Zhijian and Bas, Erhan and Xiao, Cao and Prasanna, Prateek and Bhatia, Parminder and Kass-Hout, Taha},
    title     = {Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20990-21000}
}

raw_affiliation:
[{"affiliation": "GE Healthcare", "city": "Chicago", "country": "USA"}, {"affiliation": "Department of Computer Science, Stony Brook University", "city": "Stony Brook", "country": "USA"}, {"affiliation": "Department of Biomedical Informatics, Stony Brook University", "city": "Stony Brook", "country": "USA"}]

affiliations:
["GE Healthcare", "Department of Computer Science, Stony Brook University", "Department of Biomedical Informatics, Stony Brook University"]


title: CamPoint: Boosting Point Cloud Segmentation with Virtual Camera
authors: Jianhui Zhang, Yizhi Luo, Zicheng Zhang, Xuecheng Nie, Bonan Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11822-11832
affiliations: ["University of Chinese Academy of Sciences", "JD Retail", "MT Lab, Meitu Inc."]
cities: ["Beijing", "Beijing", "Xiamen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_CamPoint_Boosting_Point_Cloud_Segmentation_with_Virtual_Camera_CVPR_2025_paper.html

abstract:
Local features aggregation and global information perception are the fundamental to point cloud segmentation. However, existing works often fall short in effectively identifying semantic relevant neighbors and face challenges in endowing each point with high-level information. Here, we propose CamPoint, an innovative method that employs virtual cameras to solve above problems. The core of CamPoint lies in introducing the novel camera visibility feature for points, where each dimension encodes the visibility of that point from a specific camera. Leveraging this feature, we propose the camera perspective slice distance for accurate relevant neighbor searching and design the camera parameter embedding to deliver rich feature representations for global interaction. Specifically, the camera perspective slice distance between two points is defined as a similarity metric derived from their camera visibility features, whereby an increased number of shared cameras observing both points corresponds to a reduced distance between them. To effectively facilitate global semantic perception, we assign each camera an optimizable embedding and then integrate these embeddings into the original spatial features based on visibility attributes, thereby obtaining high-level features enriched with camera priors. Additionally, the state space model characterized by linear computational complexity is employed as the operator to achieve global learning with efficiency. Comprehensive experiments on multiple datasets shows that our CamPoint surpasses the current state-of-the-art in multiple datasets, achieving low training cost and fast inference speed.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_CamPoint_Boosting_Point_Cloud_Segmentation_with_Virtual_Camera_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Jianhui and Luo, Yizhi and Zhang, Zicheng and Nie, Xuecheng and Li, Bonan},
    title     = {CamPoint: Boosting Point Cloud Segmentation with Virtual Camera},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11822-11832}
}

raw_affiliation:
[{"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "JD Retail", "city": "Beijing", "country": "China"}, {"affiliation": "MT Lab, Meitu Inc.", "city": "Xiamen", "country": "China"}]


title: LightLoc: Learning Outdoor LiDAR Localization at Light Speed
authors: Wen Li, Chen Liu, Shangshu Yu, Dunqiang Liu, Yin Zhou, Siqi Shen, Chenglu Wen, Cheng Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6680-6689
cities: ["Xiamen", "Xiamen", "Singapore", "Unknown"]
countries: ["China", "China", "Singapore", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_LightLoc_Learning_Outdoor_LiDAR_Localization_at_Light_Speed_CVPR_2025_paper.html

abstract:
Scene coordinate regression achieves impressive results in outdoor LiDAR localization but requires days of training. Since training needs to be repeated for each new scene, long training times make these impractical for applications requiring time-sensitive system upgrades, such as autonomous driving, drones, robotics, etc. We identify large coverage areas and vast amounts of data in large-scale outdoor scenes as key challenges that limit fast training. In this paper, we propose LightLoc, the first method capable of efficiently learning localization in a new scene at light speed. Beyond freezing the scene-agnostic feature backbone and training only the scene-specific prediction heads, we introduce two novel techniques to address these challenges. First, we introduce sample classification guidance to assist regression learning, reducing ambiguity from similar samples and improving training efficiency. Second, we propose redundant sample downsampling to remove well-learned frames during training, reducing training time without compromising accuracy. In addition, the fast training and confidence estimation characteristics of sample classification enable its integration into SLAM, effectively eliminating error accumulation. Extensive experiments on large-scale outdoor datasets demonstrate that LightLoc achieves state-of-the-art performance with just 1 hour of training--50x faster than existing methods. Our Code is available at https://github.com/liw95/LightLoc.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_LightLoc_Learning_Outdoor_LiDAR_Localization_at_Light_Speed_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_LightLoc_Learning_Outdoor_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Wen and Liu, Chen and Yu, Shangshu and Liu, Dunqiang and Zhou, Yin and Shen, Siqi and Wen, Chenglu and Wang, Cheng},
    title     = {LightLoc: Learning Outdoor LiDAR Localization at Light Speed},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6680-6689}
}

raw_affiliation:
[{"affiliation": "Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "Key Laboratory of Multimedia Trusted Perception and Ef\ufb01cient Computing, Ministry of Education of China, Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "Nanyang T echnological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "GAC R&D Center", "city": "Unknown", "country": "Unknown"}]

affiliations:
["Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University", "Key Laboratory of Multimedia Trusted Perception and Ef\ufb01cient Computing, Ministry of Education of China, Xiamen University", "Nanyang T echnological University", "GAC R&D Center"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15611-15620
affiliations: ["Stony Brook University", "Indiana University School of Medicine"]
cities: ["Stony Brook", "Indianapolis"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ganguly_MERGE_Multi-faceted_Hierarchical_Graph-based_GNN_for_Gene_Expression_Prediction_from_CVPR_2025_paper.html

title:
MERGE: Multi-faceted Hierarchical Graph-based GNN for Gene Expression Prediction from Whole Slide Histopathology Images

authors:
Aniruddha Ganguly, Debolina Chatterjee, Wentao Huang, Jie Zhang, Alisa Yurovsky, Travis Steele Johnson, Chao Chen

abstract:
Recent advances in Spatial Transcriptomics (ST) pair histology images with spatially resolved gene expression profiles, enabling predictions of gene expression across different tissue locations based on image patches. This opens up new possibilities for enhancing whole slide image (WSI) prediction tasks with localized gene expression. However, existing methods fail to fully leverage the interactions between different tissue locations, which are crucial for accurate joint prediction. To address this, we introduce MERGE (Multi-faceted hiErarchical gRaph for Gene Expressions), which combines a multi-faceted hierarchical graph construction strategy with graph neural networks (GNN) to improve gene expression predictions from WSIs. By clustering tissue image patches based on both spatial and morphological features, and incorporating intra- and inter-cluster edges, our approach fosters interactions between distant tissue locations during GNN learning. As an additional contribution, we evaluate different data smoothing techniques that are necessary to mitigate artifacts in ST data, often caused by technical imperfections. We advocate for adopting gene-aware smoothing methods that are more biologically justified. Experimental results on gene expression prediction show that our GNN method outperforms state-of-the-art techniques across multiple metrics.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ganguly_MERGE_Multi-faceted_Hierarchical_Graph-based_GNN_for_Gene_Expression_Prediction_from_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ganguly_MERGE_Multi-faceted_Hierarchical_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ganguly_2025_CVPR,
    author    = {Ganguly, Aniruddha and Chatterjee, Debolina and Huang, Wentao and Zhang, Jie and Yurovsky, Alisa and Johnson, Travis Steele and Chen, Chao},
    title     = {MERGE: Multi-faceted Hierarchical Graph-based GNN for Gene Expression Prediction from Whole Slide Histopathology Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15611-15620}
}

raw_affiliation:
[{"affiliation": "Stony Brook University", "city": "Stony Brook", "country": "USA"}, {"affiliation": "Indiana University School of Medicine", "city": "Indianapolis", "country": "USA"}]


title: Accurate Differential Operators for Hybrid Neural Fields
authors: Aditya Chetan, Guandao Yang, Zichen Wang, Steve Marschner, Bharath Hariharan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 530-539
raw_affiliation: [{"affiliation": "Cornell University", "city": "Ithaca", "country": "USA"}]
affiliations: ["Cornell University"]
cities: ["Ithaca"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chetan_Accurate_Differential_Operators_for_Hybrid_Neural_Fields_CVPR_2025_paper.html

abstract:
Neural fields have become widely used in various fields, from shape representation to neural rendering, and for solving partial differential equations (PDEs). With the advent of hybrid neural field representations like Instant NGP that leverage small MLPs and explicit representations, these models train quickly and can fit large scenes. Yet in many applications like rendering and simulation, hybrid neural fields can cause noticeable and unreasonable artifacts. This is because they do not yield accurate spatial derivatives needed for these downstream applications. In this work, we propose two ways to circumvent these challenges. Our first approach is a post hoc operator that uses local polynomial fitting to obtain more accurate derivatives from pre-trained hybrid neural fields. Additionally, we also propose a self-supervised fine-tuning approach that refines the hybrid neural field to yield accurate derivatives directly while preserving the initial signal. We show applications of our method to rendering, collision simulation, and solving PDEs. We observe that using our approach yields more accurate derivatives, reducing artifacts and leading to more accurate simulations in downstream applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chetan_Accurate_Differential_Operators_for_Hybrid_Neural_Fields_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chetan_Accurate_Differential_Operators_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chetan_2025_CVPR,
    author    = {Chetan, Aditya and Yang, Guandao and Wang, Zichen and Marschner, Steve and Hariharan, Bharath},
    title     = {Accurate Differential Operators for Hybrid Neural Fields},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {530-539}
}


title: FeedEdit: Text-Based Image Editing with Dynamic Feedback Regulation
authors: Fengyi Fu, Lei Zhang, Mengqi Huang, Zhendong Mao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2661-2670
affiliations: ["University of Science and Technology of China"]
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fu_FeedEdit_Text-Based_Image_Editing_with_Dynamic_Feedback_Regulation_CVPR_2025_paper.html

abstract:
Text-based image editing which aims at generating rigid or non-rigid changes to images conditioned on the given text, has recently attracted considerable interest. Previous works mainly follow the multi-step denoising diffusion paradigm, which adopts a fixed text guidance intensity (i.e., editing intensity) to inject textual features, while ignoring the step-specific editing requirements. This work argues that the editing intensity at each denoising step should be adaptively adjusted conditioned on the historical editing degree, to provide accurate text guidance for the whole denoising process. We thereby propose a novel feedback editing framework (FeedEdit), a training-free method which, explicitly exploits the feedback regulation on editing intensity to ensure precise and harmonious editing at all steps. Specifically, we design (1) Dynamic Editing Degree Perceiving module, which is based on specific frequency-domain filtering, to enhance and exploit the correlation between feature differences and editing degree for perceiving.  (2) Proportional-Integral feedback controller, to automatically map the perceived editing errors into appropriate feedback control signals. (3) Phrase-level Regulating Strategy, to achieve fine-grained function-specific regulation of textual features. Extensive experiments demonstrate the superiority of FeedEdit over existing methods in both editability and quality, especially for multi-function editing scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fu_FeedEdit_Text-Based_Image_Editing_with_Dynamic_Feedback_Regulation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fu_FeedEdit_Text-Based_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fu_2025_CVPR,
    author    = {Fu, Fengyi and Zhang, Lei and Huang, Mengqi and Mao, Zhendong},
    title     = {FeedEdit: Text-Based Image Editing with Dynamic Feedback Regulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2661-2670}
}


title: Classifier-guided CLIP Distillation for Unsupervised Multi-label Classification
authors: Dongseob Kim, Hyunjung Shim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4661-4671
affiliations: ["Samsung Electronics", "KAIST"]
cities: ["Seoul", "Daejeon"]
countries: ["Republic of Korea", "Republic of Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Classifier-guided_CLIP_Distillation_for_Unsupervised_Multi-label_Classification_CVPR_2025_paper.html

abstract:
Multi-label classification is crucial for comprehensive image understanding, yet acquiring accurate annotations is challenging and costly. To address this, a recent study suggests exploiting unsupervised multi-label classification leveraging CLIP, a powerful vision-language model. Despite CLIP's proficiency, it suffers from view-dependent predictions and inherent bias, limiting its effectiveness. We propose a novel method that addresses these issues by leveraging multiple views near target objects, guided by Class Activation Mapping (CAM) of the classifier, and debiasing pseudo-labels derived from CLIP predictions. Our Classifier-guided CLIP Distillation (CCD) enables selecting multiple local views without extra labels and debiasing predictions to enhance classification performance. Experimental results validate our method's superiority over existing techniques across diverse datasets. The code is available at https://github.com/k0u-id/CCD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Classifier-guided_CLIP_Distillation_for_Unsupervised_Multi-label_Classification_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Classifier-guided_CLIP_Distillation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Dongseob and Shim, Hyunjung},
    title     = {Classifier-guided CLIP Distillation for Unsupervised Multi-label Classification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4661-4671}
}

raw_affiliation:
[{"affiliation": "Samsung Electronics", "city": "Seoul", "country": "Republic of Korea"}, {"affiliation": "KAIST", "city": "Daejeon", "country": "Republic of Korea"}]


title: UMotion: Uncertainty-driven Human Motion Estimation from Inertial and Ultra-wideband Units
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7085-7094
affiliations: ["Nara Institute of Science and Technology, Japan"]
cities: ["Nara"]
countries: ["Japan"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_UMotion_Uncertainty-driven_Human_Motion_Estimation_from_Inertial_and_Ultra-wideband_Units_CVPR_2025_paper.html

authors:
Huakun Liu, Hiroki Ota, Xin Wei, Yutaro Hirao, Monica Perusquia-Hernandez, Hideaki Uchiyama, Kiyoshi Kiyokawa

abstract:
Sparse wearable inertial measurement units (IMUs) have gained popularity for estimating 3D human motion. However, challenges such as pose ambiguity, data drift, and limited adaptability to diverse bodies persist. To address these issues, we propose UMotion, an uncertainty-driven, online fusing-all state estimation framework for 3D human shape and pose estimation, supported by six integrated, body-worn ultra-wideband (UWB) distance sensors with IMUs. UWB sensors measure inter-node distances to infer spatial relationships, aiding in resolving pose ambiguities and body shape variations when combined with anthropometric data. Unfortunately, IMUs are prone to drift, and UWB sensors are affected by body occlusions. Consequently, we develop a tightly coupled Unscented Kalman Filter (UKF) framework that fuses uncertainties from sensor data and estimated human motion based on individual body shape. The UKF iteratively refines IMU and UWB measurements by aligning them with uncertain human motion constraints in real-time, producing optimal estimates for each. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of UMotion in stabilizing sensor data and the improvement over state of the art in pose accuracy. Code is available at: https://github.com/kk9six/umotion.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_UMotion_Uncertainty-driven_Human_Motion_Estimation_from_Inertial_and_Ultra-wideband_Units_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_UMotion_Uncertainty-driven_Human_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Huakun and Ota, Hiroki and Wei, Xin and Hirao, Yutaro and Perusquia-Hernandez, Monica and Uchiyama, Hideaki and Kiyokawa, Kiyoshi},
    title     = {UMotion: Uncertainty-driven Human Motion Estimation from Inertial and Ultra-wideband Units},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7085-7094}
}

raw_affiliation:
[{"affiliation": "Nara Institute of Science and Technology, Japan", "city": "Nara", "country": "Japan"}]


authors: Koushik Srivatsan, Fahad Shamshad, Muzammal Naseer, Vishal M. Patel, Karthik Nandakumar
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23765-23774
affiliations: ["Johns Hopkins University", "MBZUAI", "Khalifa University", "Michigan State University"]
cities: ["Baltimore", "Abu Dhabi", "Abu Dhabi", "East Lansing"]
countries: ["USA", "UAE", "UAE", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Srivatsan_STEREO_A_Two-Stage_Framework_for_Adversarially_Robust_Concept_Erasing_from_CVPR_2025_paper.html

title:
STEREO: A Two-Stage Framework for Adversarially Robust Concept Erasing from Text-to-Image Diffusion Models

abstract:
The rapid proliferation of large-scale text-to-image diffusion (T2ID) models has raised serious concerns about their potential misuse in generating harmful content.  Although numerous methods have been proposed for erasing undesired concepts from T2ID models, they often provide a false sense of security; concept-erased models (CEMs) can still be manipulated via adversarial attacks to regenerate the erased concept. While a few robust concept erasure methods based on adversarial training have emerged recently, they compromise on utility (generation quality for benign concepts) to achieve robustness and/or remain vulnerable to advanced embedding space attacks. These limitations stem from the failure of robust CEMs to thoroughly search for blind spots in the embedding space. To bridge this gap, we propose STEREO, a novel two-stage framework that employs adversarial training as a first step rather than the only step for robust concept erasure. In the first stage, STEREO employs adversarial training as a vulnerability identification mechanism to search thoroughly enough. In the second robustly erase once stage, STEREO introduces an anchor-concept-based compositional objective to robustly erase the target concept in a single fine-tuning stage, while minimizing the degradation of model utility. We benchmark STEREO against seven state-of-the-art concept erasure methods, demonstrating its superior robustness to both white-box and black-box attacks, while largely preserving utility.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Srivatsan_STEREO_A_Two-Stage_Framework_for_Adversarially_Robust_Concept_Erasing_from_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Srivatsan_STEREO_A_Two-Stage_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Srivatsan_2025_CVPR,
    author    = {Srivatsan, Koushik and Shamshad, Fahad and Naseer, Muzammal and Patel, Vishal M. and Nandakumar, Karthik},
    title     = {STEREO: A Two-Stage Framework for Adversarially Robust Concept Erasing from Text-to-Image Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23765-23774}
}

raw_affiliation:
[{"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}, {"affiliation": "MBZUAI", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "Khalifa University", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "Michigan State University", "city": "East Lansing", "country": "USA"}]


title: Scene Map-based Prompt Tuning for Navigation Instruction Generation
authors: Sheng Fan, Rui Liu, Wenguan Wang, Yi Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6898-6908
raw_affiliation: [{"affiliation": "ReLER, CCAI, Zhejiang University", "city": "Hangzhou", "country": "China"}]
affiliations: ["ReLER, CCAI, Zhejiang University"]
cities: ["Hangzhou"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fan_Scene_Map-based_Prompt_Tuning_for_Navigation_Instruction_Generation_CVPR_2025_paper.html

abstract:
Navigation instruction generation (NIG), which provides interactive feedback and guidance to humans along a trajectory, is vital for developing embodied agents capable of human-machine communication and collaboration through natural language. Early data-driven methods directly map sequences of past observations to trajectory descriptions on limited datasets, lacking the necessary spatial understanding in complex 3D environments. While recent approaches leverage Large Language Models (LLMs) to improve NIG, they often overlook the global spatial context in navigation, such as the inherent space discretization in maps. Instead of straightforwardly feeding textual descriptions of the map into LLMs, we propose a scene map-based prompt tuning framework for NIG, MAPInstructor, which incorporates map context for parameter-efficient updating of LLMs. MAPInstructor comprises three key components: (i) scene representation encoding, where egocentric observations are projected into 3D voxels for fine-grained scene understanding; (ii) map prompt tuning, which integrates a topological map representation of the entire trajectory into an LLM-based decoder; and (iii) landmark uncertainty assessment, which mitigates hallucinations in landmark predictions, thereby enhancing the reliability and coherence of instruction generation. Extensive experiments on three navigation datasets (i.e., R2R, REVERIE, RxR) confirm the generalization and effectiveness of our algorithm.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fan_Scene_Map-based_Prompt_Tuning_for_Navigation_Instruction_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fan_Scene_Map-based_Prompt_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fan_2025_CVPR,
    author    = {Fan, Sheng and Liu, Rui and Wang, Wenguan and Yang, Yi},
    title     = {Scene Map-based Prompt Tuning for Navigation Instruction Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6898-6908}
}


title: GenVDM: Generating Vector Displacement Maps From a Single Image
authors: Yuezhi Yang, Qimin Chen, Vladimir G. Kim, Siddhartha Chaudhuri, Qixing Huang, Zhiqin Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26618-26629
affiliations: ["The University of Texas at Austin", "Simon Fraser University", "Adobe Research"]
cities: ["Austin", "Burnaby", "San Jose"]
countries: ["USA", "Canada", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_GenVDM_Generating_Vector_Displacement_Maps_From_a_Single_Image_CVPR_2025_paper.html

abstract:
We introduce the first method for generating Vector Displacement Maps (VDMs): parameterized, detailed geometric stamps commonly used in 3D modeling. Given a single input image, our method first generates multi-view normal maps and then reconstructs a VDM from the normals via a novel reconstruction pipeline. We also propose an efficient algorithm for extracting VDMs from 3D objects, and present the first academic VDM dataset. Compared to existing 3D generative models focusing on complete shapes, we focus on generating parts that can be seamlessly attached to shape surfaces. The method gives artists rich control over adding geometric details to a 3D shape. Experiments demonstrate that our approach outperforms existing baselines. Generating VDMs offers additional benefits, such as using 2D image editing to customize and refine 3D details.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_GenVDM_Generating_Vector_Displacement_Maps_From_a_Single_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_GenVDM_Generating_Vector_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Yuezhi and Chen, Qimin and Kim, Vladimir G. and Chaudhuri, Siddhartha and Huang, Qixing and Chen, Zhiqin},
    title     = {GenVDM: Generating Vector Displacement Maps From a Single Image},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26618-26629}
}

raw_affiliation:
[{"affiliation": "The University of Texas at Austin", "city": "Austin", "country": "USA"}, {"affiliation": "Simon Fraser University", "city": "Burnaby", "country": "Canada"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}]


title: DropoutGS: Dropping Out Gaussians for Better Sparse-view Rendering
authors: Yexing Xu, Longguang Wang, Minglin Chen, Sheng Ao, Li Li, Yulan Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 701-710
affiliations: ["The Shenzhen Campus, Sun Yat-Sen University", "Xiamen University", "University of Macau"]
cities: ["Shenzhen", "Xiamen", "Macau"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_DropoutGS_Dropping_Out_Gaussians_for_Better_Sparse-view_Rendering_CVPR_2025_paper.html

abstract:
Although 3D Gaussian Splatting (3DGS) has demonstrated promising results in novel view synthesis, its performance degrades dramatically with sparse inputs and generates undesirable artifacts. As the number of training views decreases, the novel view synthesis task degrades to a highly under-determined problem such that existing methods suffer from the notorious overfitting issue. Interestingly, we observe that models with fewer Gaussian primitives exhibit less overfitting under spare inputs. Inspired by this observation, we propose a Random Dropout Regularization (RDR) to exploit the advantages of low-complexity models to alleviate overfitting. In addition, to remedy the lack of high-frequency details for these models, an Edge-guided Splitting Strategy (ESS) is developed. With these two techniques, our method (termed DropoutGS) provides a simple yet effective plug-in approach to improve the generalization performance of existing 3DGS methods. Extensive experiments show that our DropoutGS produces state-of-the-art performance under sparse views on benchmark datasets including Blender, LLFF, and DTU.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_DropoutGS_Dropping_Out_Gaussians_for_Better_Sparse-view_Rendering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_DropoutGS_Dropping_Out_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Yexing and Wang, Longguang and Chen, Minglin and Ao, Sheng and Li, Li and Guo, Yulan},
    title     = {DropoutGS: Dropping Out Gaussians for Better Sparse-view Rendering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {701-710}
}

raw_affiliation:
[{"affiliation": "The Shenzhen Campus, Sun Yat-Sen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "University of Macau", "city": "Macau", "country": "China"}]


title: Effective SAM Combination for Open-Vocabulary Semantic Segmentation
authors: Minhyeok Lee, Suhwan Cho, Jungho Lee, Sunghun Yang, Heeseung Choi, Ig-Jae Kim, Sangyoun Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26081-26090
affiliations: ["Yonsei University", "Korea Institute of Science and Technology (KIST)"]
cities: ["Seoul", "Seoul"]
countries: ["South Korea", "South Korea"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Effective_SAM_Combination_for_Open-Vocabulary_Semantic_Segmentation_CVPR_2025_paper.html

abstract:
Open-vocabulary semantic segmentation aims to assign pixel-level labels to images across an unlimited range of classes. Traditional methods address this by sequentially connecting a powerful mask proposal generator, such as the Segment Anything Model (SAM), with a pre-trained vision-language model like CLIP. But these two-stage approaches often suffer from high computational costs, memory inefficiencies. In this paper, we propose ESC-Net, a novel one-stage open-vocabulary segmentation model that leverages the SAM decoder blocks for class-agnostic segmentation within an efficient inference framework. By embedding pseudo prompts generated from image-text correlations into SAM's promptable segmentation framework, ESC-Net achieves refined spatial aggregation for accurate mask predictions. Additionally, a Vision-Language Fusion (VLF) module enhances the final mask prediction through image and text guidance. ESC-Net achieves superior performance on standard benchmarks, including ADE20K, PASCAL-VOC, and PASCAL-Context, outperforming prior methods in both efficiency and accuracy. Comprehensive ablation studies further demonstrate its robustness across challenging conditions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Effective_SAM_Combination_for_Open-Vocabulary_Semantic_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Effective_SAM_Combination_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Minhyeok and Cho, Suhwan and Lee, Jungho and Yang, Sunghun and Choi, Heeseung and Kim, Ig-Jae and Lee, Sangyoun},
    title     = {Effective SAM Combination for Open-Vocabulary Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26081-26090}
}

raw_affiliation:
[{"affiliation": "Yonsei University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Korea Institute of Science and Technology (KIST)", "city": "Seoul", "country": "South Korea"}]


title: Bridging Modalities: Improving Universal Multimodal Retrieval by Multimodal Large Language Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9274-9285
affiliations: ["The Hong Kong Polytechnic University", "Tongyi Lab, Alibaba Group", "Soochow University"]
cities: ["Hong Kong", "Hangzhou", "Suzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Bridging_Modalities_Improving_Universal_Multimodal_Retrieval_by_Multimodal_Large_Language_CVPR_2025_paper.html

authors:
Xin Zhang, Yanzhao Zhang, Wen Xie, Mingxin Li, Ziqi Dai, Dingkun Long, Pengjun Xie, Meishan Zhang, Wenjie Li, Min Zhang

abstract:
Universal Multimodal Retrieval (UMR) aims to enable search across various modalities using a unified model, where queries and candidates can consist of pure text, images, or a combination of both. Previous work has attempted to adopt multimodal large language models (MLLMs) to realize UMR using only text data. However, our preliminary experiments demonstrate that more diverse multimodal training data can further unlock the potential of MLLMs. Despite its effectiveness, the existing multimodal training data is highly imbalanced in terms of modality, which motivates us to develop a training data synthesis pipeline and construct a large-scale, high-quality fused-modal training dataset. Based on the synthetic training data, we develop the General Multimodal Embedder (GME), an MLLM-based dense retriever designed for UMR. Furthermore, we construct a comprehensive UMR Benchmark (UMRB) to evaluate the effectiveness of our approach. Experimental results show that our method achieves state-of-the-art performance among existing UMR methods. Last, we provide in-depth analyses of model scaling and training strategies, and perform ablation studies on both the model and synthetic data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Bridging_Modalities_Improving_Universal_Multimodal_Retrieval_by_Multimodal_Large_Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Bridging_Modalities_Improving_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Xin and Zhang, Yanzhao and Xie, Wen and Li, Mingxin and Dai, Ziqi and Long, Dingkun and Xie, Pengjun and Zhang, Meishan and Li, Wenjie and Zhang, Min},
    title     = {Bridging Modalities: Improving Universal Multimodal Retrieval by Multimodal Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9274-9285}
}

raw_affiliation:
[{"affiliation": "The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}, {"affiliation": "Tongyi Lab, Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Soochow University", "city": "Suzhou", "country": "China"}]


title: Enhancing Dataset Distillation via Non-Critical Region Refinement
authors: Minh-Tuan Tran, Trung Le, Xuan-May Le, Thanh-Toan Do, Dinh Phung
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10015-10024
affiliations: ["Monash University", "The University of Melbourne"]
cities: ["Melbourne", "Melbourne"]
countries: ["Australia", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tran_Enhancing_Dataset_Distillation_via_Non-Critical_Region_Refinement_CVPR_2025_paper.html

abstract:
Dataset distillation has gained popularity as a technique for compressing large datasets into smaller, more efficient representations while retaining essential information for model training.  Data features can be broadly divided into two types: instance-specific features, which capture unique, fine-grained details of individual examples, and class-general features, which represent shared, broad patterns across a class. However, previous approaches often struggle to balance these; some focus solely on class-general features, missing finer instance details, while others concentrate on instance-specific features, overlooking the shared characteristics essential for class-level understanding. In this paper, we propose the Non-Critical Region Refinement Dataset Distillation (NRR-DD) method, which preserves the instance-specific and fine-grained regions in synthetic data while enriching non-critical regions with more class-general information. This approach enables our models to leverage all pixel information to capture both types of features, thereby improving overall performance. Furthermore, we introduce Distance-Based Representative (DBR) knowledge transfer, which eliminates the need for soft labels in training by relying solely on the distance between synthetic data predictions and one-hot encoded labels. Experimental results demonstrate that our NRR-DD achieves state-of-the-art performance on both small-scale and large-scale datasets. Additionally, by storing only two distances per instance, our method achieves comparable results across various settings. Code will be available at https://github.com/tmtuan1307/NRR-DD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tran_Enhancing_Dataset_Distillation_via_Non-Critical_Region_Refinement_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tran_Enhancing_Dataset_Distillation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tran_2025_CVPR,
    author    = {Tran, Minh-Tuan and Le, Trung and Le, Xuan-May and Do, Thanh-Toan and Phung, Dinh},
    title     = {Enhancing Dataset Distillation via Non-Critical Region Refinement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10015-10024}
}

raw_affiliation:
[{"affiliation": "Monash University", "city": "Melbourne", "country": "Australia"}, {"affiliation": "The University of Melbourne", "city": "Melbourne", "country": "Australia"}]


authors: Wenqiao Li, Yao Gu, Xintao Chen, Xiaohao Xu, Ming Hu, Xiaonan Huang, Yingna Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30409-30419
affiliations: ["ShanghaiTech University", "University of Michigan, Ann Arbor", "Monash University"]
cities: ["Shanghai", "Ann Arbor", "Melbourne"]
countries: ["China", "USA", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Towards_Visual_Discrimination_and_Reasoning_of_Real-World_Physical_Dynamics_Physics-Grounded_CVPR_2025_paper.html

title:
Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detection

abstract:
Humans detect real-world object anomalies by perceiving, interacting, and reasoning based on object-conditioned physical knowledge. The long-term goal of Industrial Anomaly Detection (IAD) is to enable machines to autonomously replicate this skill. However, current IAD algorithms are largely developed and tested on static, semantically simple datasets, which diverge from real-world scenarios where physical understanding and reasoning are essential.To bridge this gap, we introduce the Physics Anomaly Detection (Phys-AD) dataset, the first large-scale, real-world, physics-grounded video dataset for industrial anomaly detection. Collected using a real robot arm and motor, Phys-AD provides a diverse set of dynamic, semantically rich scenarios. The dataset includes more than 6400 videos across 22 real-world object categories, interacting with robot arms and motors, and exhibits 47 types of anomalies. Anomaly detection in Phys-AD requires visual reasoning, combining both physical knowledge and video content to determine object abnormality.We benchmark state-of-the-art anomaly detection methods under three settings: unsupervised AD, weakly-supervised AD, and video-understanding AD, highlighting their limitations in handling physics-grounded anomalies. Additionally, we introduce the Physics Anomaly Explanation (PAEval) metric, designed to assess the ability of visual-language foundation models to not only detect anomalies but also provide accurate explanations for their underlying physical causes. Our project is available at https://guyao2023.github.io/Phys-AD/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Towards_Visual_Discrimination_and_Reasoning_of_Real-World_Physical_Dynamics_Physics-Grounded_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Towards_Visual_Discrimination_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Wenqiao and Gu, Yao and Chen, Xintao and Xu, Xiaohao and Hu, Ming and Huang, Xiaonan and Wu, Yingna},
    title     = {Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30409-30419}
}

raw_affiliation:
[{"affiliation": "ShanghaiTech University", "city": "Shanghai", "country": "China"}, {"affiliation": "University of Michigan, Ann Arbor", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "Monash University", "city": "Melbourne", "country": "Australia"}]


title: SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19867-19878
cities: ["Hefei", "Shanghai", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_SPA-VL_A_Comprehensive_Safety_Preference_Alignment_Dataset_for_Vision_Language_CVPR_2025_paper.html

authors:
Yongting Zhang, Lu Chen, Guodong Zheng, Yifeng Gao, Rui Zheng, Jinlan Fu, Zhenfei Yin, Senjie Jin, Yu Qiao, Xuanjing Huang, Feng Zhao, Tao Gui, Jing Shao

abstract:
The emergence of Vision Language Models (VLMs) has brought unprecedented advances in understanding multimodal information. The combination of textual and visual semantics in VLMs is highly complex and diverse, making the safety alignment of these models challenging. Furthermore, due to the limited study on the safety alignment of VLMs, there is a lack of large-scale, high-quality datasets. To address these limitations, we propose a Safety Preference Alignment dataset for Vision Language Models named SPA-VL. In terms of breadth, SPA-VL covers 6 harmfulness domains, 13 categories, and 53 subcategories, and contains 100,788 samples of the quadruple (question, image, chosen response, rejected response). In terms of depth, the responses are collected from 12 open-source (e.g., QwenVL) and closed-source (e.g., Gemini) VLMs to ensure diversity. The construction of preference data is fully automated, and the experimental results indicate that models trained with alignment techniques on the SPA-VL dataset exhibit substantial improvements in harmlessness and helpfulness while maintaining core capabilities. SPA-VL, as a large-scale, high-quality, and diverse dataset, represents a significant milestone in ensuring that VLMs achieve both harmlessness and helpfulness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_SPA-VL_A_Comprehensive_Safety_Preference_Alignment_Dataset_for_Vision_Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_SPA-VL_A_Comprehensive_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Yongting and Chen, Lu and Zheng, Guodong and Gao, Yifeng and Zheng, Rui and Fu, Jinlan and Yin, Zhenfei and Jin, Senjie and Qiao, Yu and Huang, Xuanjing and Zhao, Feng and Gui, Tao and Shao, Jing},
    title     = {SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19867-19878}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}]

affiliations:
["University of Science and Technology of China", "Fudan University", "Shanghai Artificial Intelligence Laboratory"]


title: PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting
authors: Alex Hanson, Allen Tu, Vasu Singla, Mayuka Jayawardhana, Matthias Zwicker, Tom Goldstein
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5949-5958
raw_affiliation: [{"affiliation": "University of Maryland", "city": "College Park", "country": "USA"}]
affiliations: ["University of Maryland"]
cities: ["College Park"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hanson_PUP_3D-GS_Principled_Uncertainty_Pruning_for_3D_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
Recent advances in novel view synthesis have enabled real-time rendering speeds with high reconstruction accuracy. 3D Gaussian Splatting (3D-GS), a foundational point-based parametric 3D scene representation, models scenes as large sets of 3D Gaussians. However, complex scenes can consist of millions of Gaussians, resulting in high storage and memory requirements that limit the viability of 3D-GS on devices with limited resources. Current techniques for compressing these pretrained models by pruning Gaussians rely on combining heuristics to determine which Gaussians to remove. At high compression ratios, these pruned scenes suffer from heavy degradation of visual fidelity and loss of foreground details. In this paper, we propose a principled sensitivity pruning score that preserves visual fidelity and foreground details at significantly higher compression ratios than existing approaches. It is computed as a second-order approximation of the reconstruction error on the training views with respect to the spatial parameters of each Gaussian. Additionally, we propose a multi-round prune-refine pipeline that can be applied to any pretrained 3D-GS model without changing its training pipeline. After pruning 90% of Gaussians, a substantially higher percentage than previous methods, our PUP 3D-GS pipeline increases average rendering speed by 3.56x while retaining more salient foreground information and achieving higher image quality metrics than existing techniques on scenes from the Mip-NeRF 360, Tanks & Temples, and Deep Blending datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hanson_PUP_3D-GS_Principled_Uncertainty_Pruning_for_3D_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hanson_PUP_3D-GS_Principled_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hanson_2025_CVPR,
    author    = {Hanson, Alex and Tu, Allen and Singla, Vasu and Jayawardhana, Mayuka and Zwicker, Matthias and Goldstein, Tom},
    title     = {PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5949-5958}
}


title: UniAP: Unifying Inter- and Intra-Layer Automatic Parallelism by Mixed Integer Quadratic Programming
authors: Hao Lin, Ke Wu, Jie Li, Jun Li, Wu-Jun Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20947-20957
cities: ["Nanjing"]
countries: ["China"]
oral: yes
award_candidate: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_UniAP_Unifying_Inter-_and_Intra-Layer_Automatic_Parallelism_by_Mixed_Integer_CVPR_2025_paper.html

abstract:
Distributed learning is commonly used for training deep learning models, especially large models. In distributed learning, manual parallelism (MP) methods demand considerable human effort and have limited flexibility. Hence, automatic parallelism (AP) methods have recently been proposed for automating the parallel strategy optimization process. Existing AP methods suffer from sub-optimal solutions because they do not jointly optimize the two categories of parallel strategies (i.e., inter-layer parallelism and intra-layer parallelism). In this paper, we propose a novel AP method called UniAP, which unifies inter- and intra-layer automatic parallelism by mixed integer quadratic programming. To the best of our knowledge, UniAP is the first parallel method that can jointly optimize the two categories of parallel strategies to find an optimal solution. Experimental results show that UniAP outperforms state-of-the-art methods by up to 3.80x in throughput and reduces strategy optimization time by up to 107x across five Transformer-based models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_UniAP_Unifying_Inter-_and_Intra-Layer_Automatic_Parallelism_by_Mixed_Integer_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_UniAP_Unifying_Inter-_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Hao and Wu, Ke and Li, Jie and Li, Jun and Li, Wu-Jun},
    title     = {UniAP: Unifying Inter- and Intra-Layer Automatic Parallelism by Mixed Integer Quadratic Programming},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20947-20957}
}

raw_affiliation:
[{"affiliation": "National Key Laboratory for Novel Software Technology, School of Computer Science, Nanjing University", "city": "Nanjing", "country": "China"}]

affiliations:
["National Key Laboratory for Novel Software Technology, School of Computer Science, Nanjing University"]


authors: Xiang Gao, Shuai Yang, Jiaying Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18240-18249
affiliations: ["Wangxuan Institute of Computer Technology, Peking University"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_PTDiffusion_Free_Lunch_for_Generating_Optical_Illusion_Hidden_Pictures_with_CVPR_2025_paper.html

title:
PTDiffusion: Free Lunch for Generating Optical Illusion Hidden Pictures with Phase-Transferred Diffusion Model

abstract:
Optical illusion hidden picture is an interesting visual perceptual phenomenon where an image is cleverly integrated into another picture. Established on the off-the-shelf text-to-image (T2I) diffusion model, we propose a novel text-guided image-to-image (I2I) translation framework dubbed as Phase-Transferred Diffusion Model (PTDiffusion) for hidden art syntheses, which harmoniously embeds an input reference image into arbitrary scenes described by the text prompts. At the heart of our method is a plug-and-play phase transfer mechanism that dynamically and progressively transplants diffusion features' phase spectrum from the denoising process to reconstruct the reference image into the one to sample the generated illusion image, realizing deep fusion of the reference structural information and the textual semantic information. Furthermore, we propose asynchronous phase transfer to enable flexible control over the degree of hidden content discernability. Our method bypasses any model training and fine-tuning process, all while substantially outperforming related methods in image quality, text fidelity, visual discernibility, and contextual naturalness for illusion picture synthesis, as demonstrated by extensive qualitative and quantitative experiments. Our project is publically available at \href https://xianggao1102.github.io/PTDiffusion_webpage/  this web page .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_PTDiffusion_Free_Lunch_for_Generating_Optical_Illusion_Hidden_Pictures_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_PTDiffusion_Free_Lunch_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Xiang and Yang, Shuai and Liu, Jiaying},
    title     = {PTDiffusion: Free Lunch for Generating Optical Illusion Hidden Pictures with Phase-Transferred Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18240-18249}
}


title: ScribbleLight: Single Image Indoor Relighting with Scribbles
authors: Jun Myeong Choi, Annie Wang, Pieter Peers, Anand Bhattad, Roni Sengupta
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5720-5731
cities: ["Chapel Hill", "Williamsburg", "Chicago"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Choi_ScribbleLight_Single_Image_Indoor_Relighting_with_Scribbles_CVPR_2025_paper.html

abstract:
Image-based relighting of indoor rooms creates an immersive virtual understanding of the space, which is useful for interior design, virtual staging, and real estate. Relighting indoor rooms from a single image is especially challenging due to complex illumination interactions between multiple lights and cluttered objects featuring a large variety in geometrical and material complexity. Recently, generative models have been successfully applied to image-based relighting conditioned on a target image or a latent code, albeit without detailed local lighting control. In this paper, we introduce ScribbleLight, a generative model that supports local fine-grained control of lighting effects through scribbles that describe changes in lighting. Our key technical novelty is an Albedo-conditioned Stable Image Diffusion model that preserves the intrinsic color and texture of the original image after relighting and an encoder-decoder-based ControlNet architecture that enables geometry-preserving lighting effects with normal map and scribble annotations. We demonstrate ScribbleLight's ability to create different lighting effects (e.g., turning lights on/off, adding highlights, cast shadows, or indirect lighting from unseen lights) from sparse scribble annotations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Choi_ScribbleLight_Single_Image_Indoor_Relighting_with_Scribbles_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Choi_ScribbleLight_Single_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Choi_2025_CVPR,
    author    = {Choi, Jun Myeong and Wang, Annie and Peers, Pieter and Bhattad, Anand and Sengupta, Roni},
    title     = {ScribbleLight: Single Image Indoor Relighting with Scribbles},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5720-5731}
}

raw_affiliation:
[{"affiliation": "University of North Carolina at Chapel Hill", "city": "Chapel Hill", "country": "USA"}, {"affiliation": "College of William & Mary", "city": "Williamsburg", "country": "USA"}, {"affiliation": "Toyota Technological Institute at Chicago", "city": "Chicago", "country": "USA"}]

affiliations:
["University of North Carolina at Chapel Hill", "College of William & Mary", "Toyota Technological Institute at Chicago"]


title: Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation
authors: Tung-Long Vuong, Hoang Phan, Vy Vo, Anh Bui, Thanh-Toan Do, Trung Le, Dinh Phung
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19974-19984
affiliations: ["Monash University", "New York University"]
cities: ["Melbourne", "New York"]
countries: ["Australia", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Vuong_Preserving_Clusters_in_Prompt_Learning_for_Unsupervised_Domain_Adaptation_CVPR_2025_paper.html

abstract:
Recent approaches leveraging multi-modal pre-trained models like CLIP for Unsupervised Domain Adaptation (UDA) have shown significant promise in bridging domain gaps and improving generalization by utilizing rich semantic knowledge and robust visual representations learned through extensive pre-training on diverse image-text datasets. While these methods achieve state-of-the-art performance across benchmarks, much of the improvement stems from base pseudo-labels (CLIP zero-shot predictions) and self-training mechanisms. Thus, the training mechanism exhibits a key limitation wherein the visual embedding distribution in target domains can deviate from the visual embedding distribution in the pre-trained model, leading to misguided signals from class descriptions. This work introduces a fresh solution to reinforce these pseudo-labels and facilitate target-prompt learning, by exploiting the geometry of visual and text embeddings - an aspect that is overlooked by existing methods. We first propose to directly leverage the reference predictions (from source prompts) based on the relationship between source and target visual embeddings. We later show that there is a strong clustering behavior observed between visual and text embeddings in pre-trained multi-modal models. Building on optimal transport theory, we transform this insight into a novel strategy to enforce the clustering property in text embeddings, further enhancing the alignment in the target domain. Our experiments and ablation studies validate the effectiveness of the proposed approach, demonstrating superior performance and improved quality of target prompts in terms of representation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Vuong_Preserving_Clusters_in_Prompt_Learning_for_Unsupervised_Domain_Adaptation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Vuong_Preserving_Clusters_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Vuong_2025_CVPR,
    author    = {Vuong, Tung-Long and Phan, Hoang and Vo, Vy and Bui, Anh and Do, Thanh-Toan and Le, Trung and Phung, Dinh},
    title     = {Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19974-19984}
}

raw_affiliation:
[{"affiliation": "Monash University", "city": "Melbourne", "country": "Australia"}, {"affiliation": "New York University", "city": "New York", "country": "USA"}]


title: InsightEdit: Towards Better Instruction Following for Image Editing
authors: Yingjing Xu, Jie Kong, Jiazhi Wang, Xiao Pan, Bo Lin, Qiang Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2694-2703
affiliations: ["101.AI", "Zhejiang University"]
cities: ["Unknown", "Unknown"]
countries: ["Unknown", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_InsightEdit_Towards_Better_Instruction_Following_for_Image_Editing_CVPR_2025_paper.html

abstract:
In this paper, we focus on the task of instruction-based image editing. Previous works like InstructPix2Pix, InstructDiffusion, and SmartEdit have explored end-to-end editing. However, two limitations still remain: First, existing datasets suffer from low resolution, poor background consistency, and overly simplistic instructions. Second, current approaches mainly condition on the text while the rich image information is underexplored, therefore inferior in complex instruction following and maintaining background consistency. Targeting these issues, we first curated the AdvancedEdit dataset using a novel data construction pipeline, formulating a large-scale dataset with high visual quality, complex instructions, and good background consistency. Then, to further inject the rich image information, we introduce a two-stream bridging mechanism utilizing both the textual and visual features reasoned by the powerful Multimodal Large Language Models (MLLM) to guide the image editing process more precisely. Extensive results demonstrate that our approach, InsightEdit, achieves state-of-the-art performance, excelling in complex instruction following and maintaining high background consistency with the original image.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_InsightEdit_Towards_Better_Instruction_Following_for_Image_Editing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_InsightEdit_Towards_Better_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Yingjing and Kong, Jie and Wang, Jiazhi and Pan, Xiao and Lin, Bo and Liu, Qiang},
    title     = {InsightEdit: Towards Better Instruction Following for Image Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2694-2703}
}

raw_affiliation:
[{"affiliation": "101.AI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Zhejiang University", "city": "Unknown", "country": "China"}]


title: Attend to Not Attended: Structure-then-Detail Token Merging for Post-training DiT Acceleration
authors: Haipeng Fang, Sheng Tang, Juan Cao, Enshuo Zhang, Fan Tang, Tong-Yee Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18083-18092
cities: ["Beijing", "Beijing", "Tainan"]
countries: ["China", "China", "Taiwan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fang_Attend_to_Not_Attended_Structure-then-Detail_Token_Merging_for_Post-training_DiT_CVPR_2025_paper.html

abstract:
Diffusion transformers have shown exceptional performance in visual generation but incur high computational costs. Token reduction techniques that compress models by sharing the denoising process among similar tokens have been introduced. However, existing approaches neglect the denoising priors of the diffusion models, leading to suboptimal acceleration and diminished image quality. This study proposes a novel concept: attend to prune feature redundancies in areas not attended by the diffusion process. We analyze the location and degree of feature redundancies based on the structure-then-detail denoising priors. Subsequently, we introduce SDTM, a structure-then-detail token merging approach that dynamically compresses feature redundancies. Specifically, we design dynamic visual token merging, compression ratio adjusting, and prompt reweighting for different stages. Served in a post-training way, the proposed method can be integrated seamlessly into any DiT architecture. Extensive experiments across various backbones, schedulers, and datasets showcase the superiority of our method, for example, it achieves 1.55 times acceleration with negligible impact on image quality. Project page: https://github.com/ICTMCG/SDTM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fang_Attend_to_Not_Attended_Structure-then-Detail_Token_Merging_for_Post-training_DiT_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fang_Attend_to_Not_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fang_2025_CVPR,
    author    = {Fang, Haipeng and Tang, Sheng and Cao, Juan and Zhang, Enshuo and Tang, Fan and Lee, Tong-Yee},
    title     = {Attend to Not Attended: Structure-then-Detail Token Merging for Post-training DiT Acceleration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18083-18092}
}

raw_affiliation:
[{"affiliation": "Institute of Computing T echnology , Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "National Cheng-Kung University", "city": "Tainan", "country": "Taiwan"}]

affiliations:
["Institute of Computing T echnology , Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "National Cheng-Kung University"]


title: Turbo3D: Ultra-fast Text-to-3D Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23668-23678
affiliations: ["Carnegie Mellon University", "Massachusetts Institute of Technology", "Adobe Research"]
cities: ["Pittsburgh", "Cambridge", "San Jose"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_Turbo3D_Ultra-fast_Text-to-3D_Generation_CVPR_2025_paper.html

authors:
Hanzhe Hu, Tianwei Yin, Fujun Luan, Yiwei Hu, Hao Tan, Zexiang Xu, Sai Bi, Shubham Tulsiani, Kai Zhang

abstract:
We present Turbo3D, an ultra-fast text-to-3D system capable of generating high-quality Gaussian splatting assets in under one second. Turbo3D employs a rapid 4-step, 4-view diffusion generator, and an efficient feed-forward Gaussian reconstructor, both operating in latent space. The 4-step, 4-view generator is a student model distilled through a novel Dual-Teacher approach, which encourages the student to learn view consistency from a multi-view teacher and photo-realism from a single-view teacher. By shifting the Gaussian reconstructor's inputs from pixel space to latent space, we eliminate the extra image decoding time and halve the transformer sequence length for maximum efficiency. Our method demonstrates superior 3D generation results compared to previous baselines, while operating in a fraction of their runtime.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_Turbo3D_Ultra-fast_Text-to-3D_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hu_Turbo3D_Ultra-fast_Text-to-3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hu_2025_CVPR,
    author    = {Hu, Hanzhe and Yin, Tianwei and Luan, Fujun and Hu, Yiwei and Tan, Hao and Xu, Zexiang and Bi, Sai and Tulsiani, Shubham and Zhang, Kai},
    title     = {Turbo3D: Ultra-fast Text-to-3D Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23668-23678}
}

raw_affiliation:
[{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "Massachusetts Institute of Technology", "city": "Cambridge", "country": "USA"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}]


title: SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes
authors: Weixiao Gao, Liangliang Nan, Hugo Ledoux
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24474-24484
raw_affiliation: [{"affiliation": "Delft University of Technology", "city": "Delft", "country": "Netherlands"}]
affiliations: ["Delft University of Technology"]
cities: ["Delft"]
countries: ["Netherlands"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_SUM_Parts_Benchmarking_Part-Level_Semantic_Segmentation_of_Urban_Meshes_CVPR_2025_paper.html

abstract:
Semantic segmentation in urban scene analysis has mainly focused on images or point clouds, while textured meshes--offering richer spatial representation--remain underexplored. This paper introduces SUM Parts, the first large-scale dataset for urban textured meshes with part-level semantic labels, covering about 2.5km^2 with 21 classes. The dataset was created using our designed annotation tool, supporting both face and texture-based annotations with efficient interactive selection. We also provide a comprehensive evaluation of 3D semantic segmentation and interactive annotation methods on this dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_SUM_Parts_Benchmarking_Part-Level_Semantic_Segmentation_of_Urban_Meshes_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_SUM_Parts_Benchmarking_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Weixiao and Nan, Liangliang and Ledoux, Hugo},
    title     = {SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24474-24484}
}


title: One-for-More: Continual Diffusion Model for Anomaly Detection
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4766-4775
cities: ["Shanghai", "Shanghai", "Xiamen", "Shanghai", "Shenzhen", "Shenzhen", "Ningde", "Shanghai"]
countries: ["China", "China", "China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_One-for-More_Continual_Diffusion_Model_for_Anomaly_Detection_CVPR_2025_paper.html

authors:
Xiaofan Li, Xin Tan, Zhuo Chen, Zhizhong Zhang, Ruixin Zhang, Rizen Guo, Guanna Jiang, Yulong Chen, Yanyun Qu, Lizhuang Ma, Yuan Xie

abstract:
With the rise of generative models, there is a growing interest in unifying all tasks within a generative framework. Anomaly detection methods also fall into this scope and utilize diffusion models to generate or reconstruct normal samples when given arbitrary anomaly images. However, our study found that the diffusion model suffers from severe faithfulness hallucination and catastrophic forgetting, which can't meet the unpredictable pattern increments. To mitigate the above problems, we propose a continual diffusion model that uses gradient projection to achieve stable continual learning. Gradient projection deploys a regularization on the model updating by modifying the gradient towards the direction protecting the learned knowledge. But as a double-edged sword, it also requires huge memory costs brought by the Markov process. Hence, we propose an iterative singular value decomposition method based on the transitive property of linear representation, which consumes tiny memory and incurs almost no performance loss. Finally, considering the risk of over-fitting to normal images of the diffusion model, we propose an anomaly-masked network to enhance the condition mechanism of the diffusion model. For continual anomaly detection, ours achieves first place in 17/18 settings on MVTec and VisA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_One-for-More_Continual_Diffusion_Model_for_Anomaly_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_One-for-More_Continual_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Xiaofan and Tan, Xin and Chen, Zhuo and Zhang, Zhizhong and Zhang, Ruixin and Guo, Rizen and Jiang, Guanna and Chen, Yulong and Qu, Yanyun and Ma, Lizhuang and Xie, Yuan},
    title     = {One-for-More: Continual Diffusion Model for Anomaly Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4766-4775}
}

raw_affiliation:
[{"affiliation": "East China Normal University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Innovation Institute", "city": "Shanghai", "country": "China"}, {"affiliation": "Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Tencent YouTu Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tencent WeChatPay Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "CATL", "city": "Ningde", "country": "China"}, {"affiliation": "Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China", "city": "Shanghai", "country": "China"}]

affiliations:
["East China Normal University", "Shanghai Innovation Institute", "Xiamen University", "Shanghai Jiao Tong University", "Tencent YouTu Lab", "Tencent WeChatPay Lab", "CATL", "Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China"]


title: MODA: Motion-Drift Augmentation for Inertial Human Motion Analysis
authors: Yinghao Wu, Shihui Guo, Yipeng Qin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27771-27781
cities: ["Xiamen", "Cardiff"]
countries: ["China", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_MODA_Motion-Drift_Augmentation_for_Inertial_Human_Motion_Analysis_CVPR_2025_paper.html

abstract:
While data augmentation (DA) has been extensively studied in computer vision, its application to Inertial Measurement Unit (IMU) signals remains largely unexplored, despite IMUs' growing importance in human motion analysis. In this paper, we present the first systematic study of IMU-specific data augmentation, beginning with a comprehensive analysis that identifies three fundamental properties of IMU signals: their time-series nature, inherent multimodality (rotation and acceleration) and motion-consistency characteristics. Through this analysis, we demonstrate the limitations of applying conventional time-series augmentation techniques to IMU data. We then introduce Motion-Drift Augmentation (MODA), a novel technique that simulates the natural displacement of body-worn IMUs during motion. We evaluate our approach across five diverse datasets and five deep learning settings, including i) fully-supervised, ii) semi-supervised, iii) domain adaptation, iv) domain generalization and v) few-shot learning for both Human Action Recognition (HAR) and Human Pose Estimation (HPE) tasks. Experimental results show that our proposed MODA consistently outperforms existing augmentation methods, with semi-supervised learning performance approaching state-of-the-art fully-supervised methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_MODA_Motion-Drift_Augmentation_for_Inertial_Human_Motion_Analysis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_MODA_Motion-Drift_Augmentation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Yinghao and Guo, Shihui and Qin, Yipeng},
    title     = {MODA: Motion-Drift Augmentation for Inertial Human Motion Analysis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27771-27781}
}

raw_affiliation:
[{"affiliation": "School of Informatics, Xiamen University, China", "city": "Xiamen", "country": "China"}, {"affiliation": "School of Computer Science & Informatics, Cardiff University, UK", "city": "Cardiff", "country": "UK"}]

affiliations:
["School of Informatics, Xiamen University, China", "School of Computer Science & Informatics, Cardiff University, UK"]


title: Higher-Order Ratio Cycles for Fast and Globally Optimal Shape Matching
authors: Paul Roetzer, Viktoria Ehm, Daniel Cremers, Zorah Lähner, Florian Bernard
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21793-21803
affiliations: ["University of Bonn", "Lamarr Institute", "TU Munich", "MCML"]
cities: ["Bonn", "Bonn", "Munich", "Munich"]
countries: ["Germany", "Germany", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Roetzer_Higher-Order_Ratio_Cycles_for_Fast_and_Globally_Optimal_Shape_Matching_CVPR_2025_paper.html

abstract:
In this work we address various shape matching problems that can be cast as finding cyclic paths in a product graph. This involves for example 2D-3D shape matching, 3D shape matching, or the matching of a contour to a graph. In this context, matchings are typically obtained as the minimum cost cycle in the product graph. Instead, inspired by related works on model-based image segmentation (Schoenemann and Cremers 2009), we consider minimum ratio cycles, which we combine with the recently introduced conjugate product graph in order to allow for higher-order matching costs. With that, on the one hand we avoid the bias of obtaining matchings that involve fewer/shorter edges, while on the other hand we are able to impose powerful geometric regularisation, e.g. to avoid zig-zagging. In our experiments we demonstrate that this not only leads to improved matching accuracy in most cases, but also to significantly reduced runtimes (up to two orders of magnitude, depending on the setting). Our GPU implementations are publicly available: https://github.com/paul0noah/product-graph-cycles/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Roetzer_Higher-Order_Ratio_Cycles_for_Fast_and_Globally_Optimal_Shape_Matching_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Roetzer_Higher-Order_Ratio_Cycles_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Roetzer_2025_CVPR,
    author    = {Roetzer, Paul and Ehm, Viktoria and Cremers, Daniel and L\"ahner, Zorah and Bernard, Florian},
    title     = {Higher-Order Ratio Cycles for Fast and Globally Optimal Shape Matching},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21793-21803}
}

raw_affiliation:
[{"affiliation": "University of Bonn", "city": "Bonn", "country": "Germany"}, {"affiliation": "Lamarr Institute", "city": "Bonn", "country": "Germany"}, {"affiliation": "TU Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "MCML", "city": "Munich", "country": "Germany"}]


title: Omni-RGPT: Unifying Image and Video Region-level Understanding via Token Marks
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3919-3930
affiliations: ["NVIDIA", "Yonsei University", "National Taiwan University"]
cities: ["Santa Clara", "Seoul", "Taipei"]
countries: ["USA", "South Korea", "Taiwan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Heo_Omni-RGPT_Unifying_Image_and_Video_Region-level_Understanding_via_Token_Marks_CVPR_2025_paper.html

authors:
Miran Heo, Min-Hung Chen, De-An Huang, Sifei Liu, Subhashree Radhakrishnan, Seon Joo Kim, Yu-Chiang Frank Wang, Ryo Hachiuma

abstract:
We present Omni-RGPT, a multimodal large language model designed to facilitate region-level comprehension for both images and videos. To achieve consistent region representation across spatio-temporal dimensions, we introduce Token Mark, a set of tokens highlighting the target regions within the visual feature space. These tokens are directly embedded into spatial regions using region prompts (e.g., boxes or masks) and simultaneously incorporated into the text prompt to specify the target, establishing a direct connection between visual and text tokens. To further support robust video understanding without requiring tracklets, we introduce an auxiliary task that guides Token Mark by leveraging the consistency of the tokens, enabling stable region interpretation across the video. Additionally, we introduce a large-scale region-level video instruction dataset (RegVID-300k). Omni-RGPT achieves state-of-the-art results on image and video-based commonsense reasoning benchmarks while showing strong performance in captioning and referring expression comprehension tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Heo_Omni-RGPT_Unifying_Image_and_Video_Region-level_Understanding_via_Token_Marks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Heo_Omni-RGPT_Unifying_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Heo_2025_CVPR,
    author    = {Heo, Miran and Chen, Min-Hung and Huang, De-An and Liu, Sifei and Radhakrishnan, Subhashree and Kim, Seon Joo and Wang, Yu-Chiang Frank and Hachiuma, Ryo},
    title     = {Omni-RGPT: Unifying Image and Video Region-level Understanding via Token Marks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3919-3930}
}

raw_affiliation:
[{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "Yonsei University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "National Taiwan University", "city": "Taipei", "country": "Taiwan"}]


authors: Luke Chen, Junyao Wang, Trier Mortlock, Pramod Khargonekar, Mohammad Abdullah Al Faruque
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22306-22316
raw_affiliation: [{"affiliation": "University of California, Irvine", "city": "Irvine", "country": "USA"}]
affiliations: ["University of California, Irvine"]
cities: ["Irvine"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Hyperdimensional_Uncertainty_Quantification_for_Multimodal_Uncertainty_Fusion_in_Autonomous_Vehicles_CVPR_2025_paper.html

title:
Hyperdimensional Uncertainty Quantification for Multimodal Uncertainty Fusion in Autonomous Vehicles Perception

abstract:
Uncertainty Quantification (UQ) is crucial for ensuring the reliability of machine learning models deployed in real-world autonomous systems. However, existing approaches typically quantify task-level output prediction uncertainty without considering epistemic uncertainty at the multimodal feature fusion level, leading to sub-optimal outcomes. Additionally, popular uncertainty quantification methods, e.g., Bayesian approximations, remain challenging to deploy in practice due to high computational costs in training and inference. In this paper, we propose HyperDUM, a novel deterministic uncertainty method (DUM) that efficiently quantifies feature-level epistemic uncertainty by leveraging hyperdimensional computing. Our method captures the channel and spatial uncertainties through channel and patch -wise projection and bundling techniques respectively. Multimodal sensor features are then adaptively weighted to mitigate uncertainty propagation and improve feature fusion. Our evaluations show that HyperDUM on average outperforms the state-of-the-art (SOTA) algorithms by up to 2.01%/1.27% in 3D Object Detection and up to 1.29% improvement over baselines in semantic segmentation tasks under various types of uncertainties. Notably, HyperDUM requires 2.36x less Floating Point Operations and up to 38.30x less parameters than SOTA methods, providing an efficient solution for real-world autonomous systems.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Hyperdimensional_Uncertainty_Quantification_for_Multimodal_Uncertainty_Fusion_in_Autonomous_Vehicles_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Hyperdimensional_Uncertainty_Quantification_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Luke and Wang, Junyao and Mortlock, Trier and Khargonekar, Pramod and Al Faruque, Mohammad Abdullah},
    title     = {Hyperdimensional Uncertainty Quantification for Multimodal Uncertainty Fusion in Autonomous Vehicles Perception},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22306-22316}
}


title: EDM: Equirectangular Projection-Oriented Dense Kernelized Feature Matching
authors: Dongki Jung, Jaehoon Choi, Yonghan Lee, Somi Jeong, Taejae Lee, Dinesh Manocha, Suyong Yeon
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6337-6347
affiliations: ["NA VER LABS", "University of Maryland"]
cities: ["Unknown", "College Park"]
countries: ["Unknown", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jung_EDM_Equirectangular_Projection-Oriented_Dense_Kernelized_Feature_Matching_CVPR_2025_paper.html

abstract:
We introduce the first learning-based dense matching algorithm, termed Equirectangular Projection-Oriented Dense Kernelized Feature Matching (EDM), specifically designed for omnidirectional images. Equirectangular projection (ERP) images, with their large fields of view, are particularly suited for dense matching techniques that aim to establish comprehensive correspondences across images. However, ERP images are subject to significant distortions, which we address by leveraging the spherical camera model and geodesic flow refinement in the dense matching method. To further mitigate these distortions, we propose spherical positional embeddings based on 3D Cartesian coordinates of the feature grid. Additionally, our method incorporates bidirectional transformations between spherical and Cartesian coordinate systems during refinement, utilizing a unit sphere to improve matching performance. We demonstrate that our proposed method achieves notable performance enhancements, with improvements of +26.72 and +42.62 in AUC@5deg on the Matterport3D and Stanford2D3D datasets. Project page: https://jdk9405.github.io/EDM

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jung_EDM_Equirectangular_Projection-Oriented_Dense_Kernelized_Feature_Matching_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jung_EDM_Equirectangular_Projection-Oriented_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jung_2025_CVPR,
    author    = {Jung, Dongki and Choi, Jaehoon and Lee, Yonghan and Jeong, Somi and Lee, Taejae and Manocha, Dinesh and Yeon, Suyong},
    title     = {EDM: Equirectangular Projection-Oriented Dense Kernelized Feature Matching},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6337-6347}
}

raw_affiliation:
[{"affiliation": "NA VER LABS", "city": "Unknown", "country": "Unknown"}, {"affiliation": "University of Maryland", "city": "College Park", "country": "USA"}]


title: EZSR: Event-based Zero-Shot Recognition
authors: Yan Yang, Liyuan Pan, Dongxu Li, Liu Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4628-4638
affiliations: ["BITSZ & School of CSAT, BIT", "ANU", "KooMap, Huawei"]
cities: ["Beijing", "Canberra", "Shenzhen"]
countries: ["China", "Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_EZSR_Event-based_Zero-Shot_Recognition_CVPR_2025_paper.html

abstract:
This paper studies zero-shot object recognition using event camera data. Guided by CLIP, which is pre-trained on RGB images, existing approaches achieve zero-shot object recognition by optimizing embedding similarities between event data and RGB images respectively encoded by an event encoder and the CLIP image encoder. Alternatively, several methods learn RGB frame reconstructions from event data for the CLIP image encoder. However, they often result in suboptimal zero-shot performance. This study develops an event encoder without relying on additional reconstruction networks. We theoretically analyze the performance bottlenecks of previous approaches: the embedding optimization objectives are prone to suffer from the spatial sparsity of event data, causing semantic misalignments between the learned event embedding space and the CLIP text embedding space. To mitigate the issue, we explore a scalar-wise modulation strategy. Furthermore, to scale up the number of events and RGB data pairs for training, we also study a pipeline for synthesizing event data from static RGB images in mass. Experimentally, we demonstrate an attractive scaling property in the number of parameters and synthesized data. We achieve superior zero-shot object recognition performance on extensive standard benchmark datasets, even compared with past supervised learning approaches. For example, our model with a ViT/B-16 backbone achieves 47.84% zero-shot accuracy on the N-ImageNet dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_EZSR_Event-based_Zero-Shot_Recognition_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Yan and Pan, Liyuan and Li, Dongxu and Liu, Liu},
    title     = {EZSR: Event-based Zero-Shot Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4628-4638}
}

raw_affiliation:
[{"affiliation": "BITSZ & School of CSAT, BIT", "city": "Beijing", "country": "China"}, {"affiliation": "ANU", "city": "Canberra", "country": "Australia"}, {"affiliation": "KooMap, Huawei", "city": "Shenzhen", "country": "China"}]


title: FlowRAM: Grounding Flow Matching Policy with Region-Aware Mamba Framework for Robotic Manipulation
authors: Sen Wang, Le Wang, Sanping Zhou, Jingyi Tian, Jiayi Li, Haowen Sun, Wei Tang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12176-12186
cities: ["Xi'an", "Chicago"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_FlowRAM_Grounding_Flow_Matching_Policy_with_Region-Aware_Mamba_Framework_for_CVPR_2025_paper.html

abstract:
Robotic manipulation in high-precision tasks is essential for numerous industrial and real-world applications where accuracy and speed are required. Yet current diffusion-based policy learning methods generally suffer from low computational efficiency due to the iterative denoising process during inference. Moreover, these methods do not fully explore the potential of generative models for enhancing information exploration in 3D environments. In response, we propose FlowRAM, a novel framework that leverages generative models to achieve region-aware perception, enabling efficient multimodal information processing. Specifically, we devise a Dynamic Radius Schedule, which enables adaptive perception, facilitating transitions from global scene comprehension to fine-grained geometric details. Furthermore, we integrate state space models to integrate multimodal information, while preserving linear computational complexity. In addition, we employ conditional flow matching to learn action poses by regressing deterministic vector fields, which simplifies the learning process while maintaining performance. We verify the effectiveness of the FlowRAM in the RLBench, an established manipulation benchmark, and achieve state-of-the-art performance. The results demonstrate that FlowRAM achieves a remarkable improvement, particularly in high-precision tasks, where it outperforms previous methods by 12.0% in average success rate. Additionally, FlowRAM is able to generate physically plausible actions for a variety of real-world tasks in less than 4 time steps, significantly increasing inference speed.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_FlowRAM_Grounding_Flow_Matching_Policy_with_Region-Aware_Mamba_Framework_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_FlowRAM_Grounding_Flow_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Sen and Wang, Le and Zhou, Sanping and Tian, Jingyi and Li, Jiayi and Sun, Haowen and Tang, Wei},
    title     = {FlowRAM: Grounding Flow Matching Policy with Region-Aware Mamba Framework for Robotic Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12176-12186}
}

raw_affiliation:
[{"affiliation": "National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "University of Illinois at Chicago", "city": "Chicago", "country": "USA"}]

affiliations:
["National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University", "University of Illinois at Chicago"]


title: Visual Lexicon: Rich Image Features in Language Space
authors: XuDong Wang, Xingyi Zhou, Alireza Fathi, Trevor Darrell, Cordelia Schmid
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19736-19747
affiliations: ["Google DeepMind", "UC Berkeley"]
cities: ["Mountain View", "Berkeley"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Visual_Lexicon_Rich_Image_Features_in_Language_Space_CVPR_2025_paper.html

abstract:
We present Visual Lexicon, a novel visual language that encodes rich image information into the text space of vocabulary tokens while retaining intricate visual details that are often challenging to convey in natural language. Unlike traditional methods that prioritize either high-level semantics (e.g., CLIP) or pixel-level reconstruction (e.g., VAE), ViLex simultaneously captures rich semantic content and fine visual details, enabling high-quality image generation and comprehensive visual scene understanding. Through a self-supervised learning pipeline, ViLex generates tokens optimized for reconstructing input images using a frozen text-to-image (T2I) diffusion model, preserving the detailed information necessary for high-fidelity semantic-level reconstruction. As an image embedding in the language space, ViLex tokens leverage the compositionality of natural languages, allowing them to be used independently as "text tokens" or combined with natural language tokens to prompt pretrained T2I models with both visual and textual inputs, mirroring how we interact with vision-language models (VLMs). Experiments demonstrate that ViLex achieves higher fidelity in image reconstruction compared to text embeddings--even with a single ViLex token. Moreover, ViLex successfully performs various DreamBooth tasks in a zero-shot, unsupervised manner without fine-tuning T2I models. Additionally, ViLex serves as a powerful vision encoder, consistently improving vision-language model performance across 15 benchmarks relative to a strong SigLIP baseline.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Visual_Lexicon_Rich_Image_Features_in_Language_Space_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Visual_Lexicon_Rich_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, XuDong and Zhou, Xingyi and Fathi, Alireza and Darrell, Trevor and Schmid, Cordelia},
    title     = {Visual Lexicon: Rich Image Features in Language Space},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19736-19747}
}

raw_affiliation:
[{"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}, {"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}]


title: SVFR: A Unified Framework for Generalized Video Face Restoration
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7406-7415
cities: ["Xiamen", "Unknown"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_SVFR_A_Unified_Framework_for_Generalized_Video_Face_Restoration_CVPR_2025_paper.html

authors:
Zhiyao Wang, Xu Chen, Chengming Xu, Junwei Zhu, Xiaobin Hu, Jiangning Zhang, Chengjie Wang, Yuqi Liu, Yiyi Zhou, Rongrong Ji

abstract:
Face Restoration (FR) is a crucial area within image and video processing, focusing on reconstructing high-quality portraits from degraded inputs. Despite advancements in image FR, video FR remains relatively under-explored, primarily due to challenges related to temporal consistency, motion artifacts, and the limited availability of high-quality video data. Moreover, traditional face restoration typically prioritizes enhancing resolution and may not give as much consideration to related tasks such as facial colorization and inpainting. In this paper, we propose a novel approach for the Generalized Video Face Restoration (GVFR) task, which integrates video blind face restoration (BFR), inpainting, and colorization tasks that we empirically show to benefit each other. We present a unified framework, termed as stable video face restoration (SVFR), which leverages the generative and motion priors of Stable Video Diffusion (SVD) and incorporates task-specific information through a unified face restoration framework. A learnable task embedding is introduced to enhance task identification. Meanwhile, a novel Unified Latent Regularization (ULR) is employed to encourage the shared feature representation learning among different subtasks. To further enhance the restoration quality and temporal stability, we introduce the facial prior learning and the self-referred refinement as auxiliary strategies. The proposed framework effectively combines the complementary strengths of these tasks, enhancing temporal coherence and achieving superior restoration quality. This work advances the state-of-the-art in video FR and establishes a new paradigm for generalized video face restoration. Code and video demo are available at https://github.com/wangzhiyaoo/SVFR.git.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_SVFR_A_Unified_Framework_for_Generalized_Video_Face_Restoration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_SVFR_A_Unified_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zhiyao and Chen, Xu and Xu, Chengming and Zhu, Junwei and Hu, Xiaobin and Zhang, Jiangning and Wang, Chengjie and Liu, Yuqi and Zhou, Yiyi and Ji, Rongrong},
    title     = {SVFR: A Unified Framework for Generalized Video Face Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7406-7415}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, P.R. China", "city": "Xiamen", "country": "China"}, {"affiliation": "Youtu Lab, Tencent, P.R. China", "city": "Unknown", "country": "China"}]

affiliations:
["Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, P.R. China", "Youtu Lab, Tencent, P.R. China"]


title: Decoupling Fine Detail and Global Geometry for Compressed Depth Map Super-Resolution
authors: Huan Zheng, Wencheng Han, Jianbing Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 951-960
raw_affiliation: [{"affiliation": "\u2217, \u2020", "city": "Unknown", "country": "Unknown"}]
affiliations: ["\u2217, \u2020"]
cities: ["Unknown"]
countries: ["Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_Decoupling_Fine_Detail_and_Global_Geometry_for_Compressed_Depth_Map_CVPR_2025_paper.html

abstract:
Recovering high-quality depth maps from compressed sources has gained significant attention due to the limitations of consumer-grade depth cameras and the bandwidth restrictions during data transmission. However, current methods still suffer from two challenges. First, bit-depth compression produces a uniform depth representation in regions with subtle variations, hindering the recovery of detailed information. Second, densely distributed random noise reduces the accuracy of estimating the global geometric structure of the scene. To address these challenges, we propose a novel framework, termed geometry-decoupled network (GDNet), for compressed depth map super-resolution that decouples the high-quality depth map reconstruction process by handling global and detailed geometric features separately. To be specific, we propose the fine geometry detail encoder (FGDE), which is designed to aggregate fine geometry details in high-resolution low-level image features while simultaneously enriching them with complementary information from low-resolution context-level image features. In addition, we develop the global geometry encoder (GGE) that aims at suppressing noise and extracting global geometric information effectively via constructing compact feature representation in a low-rank space. We conduct experiments on multiple benchmark datasets, demonstrating that our GDNet significantly outperforms current methods in terms of geometric consistency and detail recovery. In the ECCV 2024 AIM Compressed Depth Upsampling Challenge, our solution won the 1st place award. Our codes are available at: https://github.com/Ian0926/GDNet.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_Decoupling_Fine_Detail_and_Global_Geometry_for_Compressed_Depth_Map_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_Decoupling_Fine_Detail_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zheng_2025_CVPR,
    author    = {Zheng, Huan and Han, Wencheng and Shen, Jianbing},
    title     = {Decoupling Fine Detail and Global Geometry for Compressed Depth Map Super-Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {951-960}
}


title: Test-Time Visual In-Context Tuning
authors: Jiahao Xie, Alessio Tonioni, Nathalie Rauschmayr, Federico Tombari, Bernt Schiele
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19996-20005
affiliations: ["Max Planck Institute for Informatics, SIC 2VIA Research Center", "Google"]
cities: ["Saarbr\u00fccken", "Mountain View"]
countries: ["Germany", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Test-Time_Visual_In-Context_Tuning_CVPR_2025_paper.html

abstract:
Visual in-context learning (VICL), as a new paradigm in computer vision, allows the model to rapidly adapt to various tasks with only a handful of prompts and examples. While effective, the existing VICL paradigm exhibits poor generalizability under distribution shifts. In this work, we propose test-time Visual In-Context Tuning (VICT), a method that can adapt VICL models on the fly with a single test sample. Specifically, we flip the role between the task prompts and the test sample and use a cycle consistency loss to reconstruct the original task prompt output. Our key insight is that a model should be aware of a new test distribution if it can successfully recover the original task prompts. Extensive experiments on six representative vision tasks ranging from high-level visual understanding to low-level image processing, with 15 common corruptions, demonstrate that our VICT can improve the generalizability of VICL to unseen new domains. In addition, we show the potential of applying VICT for unseen tasks at test time. Code: https://github.com/Jiahao000/VICT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Test-Time_Visual_In-Context_Tuning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_Test-Time_Visual_In-Context_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Jiahao and Tonioni, Alessio and Rauschmayr, Nathalie and Tombari, Federico and Schiele, Bernt},
    title     = {Test-Time Visual In-Context Tuning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19996-20005}
}

raw_affiliation:
[{"affiliation": "Max Planck Institute for Informatics, SIC 2VIA Research Center", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "Google", "city": "Mountain View", "country": "USA"}]


title: Prior Does Matter: Visual Navigation via Denoising Diffusion Bridge Models
authors: Hao Ren, Yiming Zeng, Zetong Bi, Zhaoliang Wan, Junlong Huang, Hui Cheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12100-12110
cities: ["Guangzhou", "Guangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ren_Prior_Does_Matter_Visual_Navigation_via_Denoising_Diffusion_Bridge_Models_CVPR_2025_paper.html

abstract:
Recent advancements in diffusion-based imitation learning, which shows impressive performance in modeling multimodal distributions and training stability, have led to substantial progress in various robot learning tasks. In visual navigation, previous diffusion-based policies typically generate action sequences by initiating from denoising Gaussian noise. However, the target action distribution often diverges significantly from Gaussian noise, leading to redundant denoising steps and increased learning complexity. Additionally, the sparsity of effective action distributions makes it challenging for the policy to generate accurate actions without guidance. To address these issues, we propose a novel, unified visual navigation framework leveraging the denoising diffusion bridge models named NaviBridger. This approach enables action generation by initiating from any informative prior actions, enhancing guidance and efficiency in the denoising process. We explore how diffusion bridges can enhance imitation learning in visual navigation tasks and further examine three source policies for generating prior actions. Extensive experiments in both simulated and real-world indoor and outdoor scenarios demonstrate that NaviBridger accelerates policy inference and outperforms the baselines in generating target action sequences. Code is available at https://github.com/hren20/NaiviBridger.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ren_Prior_Does_Matter_Visual_Navigation_via_Denoising_Diffusion_Bridge_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ren_Prior_Does_Matter_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ren_2025_CVPR,
    author    = {Ren, Hao and Zeng, Yiming and Bi, Zetong and Wan, Zhaoliang and Huang, Junlong and Cheng, Hui},
    title     = {Prior Does Matter: Visual Navigation via Denoising Diffusion Bridge Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12100-12110}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "School of Intelligent Systems Engineering, Sun Yat-sen University", "city": "Guangzhou", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Sun Yat-sen University", "School of Intelligent Systems Engineering, Sun Yat-sen University"]


title: Digital Twin Catalog: A Large-Scale Photorealistic 3D Object Digital Twin Dataset
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 753-763
affiliations: ["Meta Reality Labs Research", "Stanford University"]
cities: ["Menlo Park", "Stanford"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dong_Digital_Twin_Catalog_A_Large-Scale_Photorealistic_3D_Object_Digital_Twin_CVPR_2025_paper.html

authors:
Zhao Dong, Ka Chen, Zhaoyang Lv, Hong-Xing Yu, Yunzhi Zhang, Cheng Zhang, Yufeng Zhu, Stephen Tian, Zhengqin Li, Geordie Moffatt, Sean Christofferson, James Fort, Xiaqing Pan, Mingfei Yan, Jiajun Wu, Carl Yuheng Ren, Richard Newcombe

abstract:
We introduce Digital Twin Catalog (DTC), a new large-scale photorealistic 3D object digital twin dataset. A digital twin of a 3D object is a highly detailed, virtually indistinguishable representation of a physical object, accurately capturing its shape, appearance, physical properties, and other attributes. Recent advances in neural-based 3D reconstruction and inverse rendering have significantly improved the quality of 3D object reconstruction. Despite these advancements, there remains a lack of a large-scale, digital twin quality real-world dataset and benchmark that can quantitatively assess and compare the performance of different reconstruction methods, as well as improve reconstruction quality through training or fine-tuning. Moreover, to democratize 3D digital twin creation, it is essential to integrate creation techniques with next-generation egocentric computing platforms, such as AR glasses. Currently, there is no dataset available to evaluate 3D object reconstruction using egocentric captured images. To address these gaps, the DTC dataset features 2,000 scanned digital twin-quality 3D objects, along with image sequences captured under different lighting conditions using DSLR cameras and egocentric AR glasses. This dataset establishes the first comprehensive real-world evaluation benchmark for 3D digital twin creation tasks, offering a robust foundation for comparing and improving existing reconstruction methods. The DTC dataset is already released at https://www.projectaria.com/datasets/dtc/ and we will also make the baseline evaluations open-source.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dong_Digital_Twin_Catalog_A_Large-Scale_Photorealistic_3D_Object_Digital_Twin_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dong_Digital_Twin_Catalog_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dong_2025_CVPR,
    author    = {Dong, Zhao and Chen, Ka and Lv, Zhaoyang and Yu, Hong-Xing and Zhang, Yunzhi and Zhang, Cheng and Zhu, Yufeng and Tian, Stephen and Li, Zhengqin and Moffatt, Geordie and Christofferson, Sean and Fort, James and Pan, Xiaqing and Yan, Mingfei and Wu, Jiajun and Ren, Carl Yuheng and Newcombe, Richard},
    title     = {Digital Twin Catalog: A Large-Scale Photorealistic 3D Object Digital Twin Dataset},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {753-763}
}

raw_affiliation:
[{"affiliation": "Meta Reality Labs Research", "city": "Menlo Park", "country": "USA"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}]


title: SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Images
authors: Kaiyu Li, Ruixun Liu, Xiangyong Cao, Xueru Bai, Feng Zhou, Deyu Meng, Zhi Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10545-10556
cities: ["Xi'an", "Xi'an", "Xi'an", "Xi'an", "Shenzhen", "Xi'an"]
countries: ["China", "China", "China", "China", "China", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_SegEarth-OV_Towards_Training-Free_Open-Vocabulary_Segmentation_for_Remote_Sensing_Images_CVPR_2025_paper.html

abstract:
Current remote sensing semantic segmentation methods are mostly built on the close-set assumption, meaning that the model can only recognize pre-defined categories that exist in the training set. However, in practical Earth observation, there are countless unseen categories, and manual annotation is impractical. To address this challenge, we first attempt to introduce training-free open-vocabulary semantic segmentation (OVSS) into the remote sensing context. However, due to the sensitivity of remote sensing images to low-resolution features, distorted target shapes and ill-fitting boundaries are exhibited in the prediction mask. To tackle these issues, we propose a simple and universal upsampler, i.e. SimFeatUp, to restore lost spatial information of deep features. Specifically, SimFeatUp only needs to learn from a few unlabeled images, and can upsample arbitrary remote sensing image features. Furthermore, based on the observation of the abnormal response of patch tokens to the [CLS] token in CLIP, we propose to execute a simple subtraction operation to alleviate the global bias in patch tokens. Extensive experiments are conducted on 17 remote sensing datasets of 4 tasks, including semantic segmentation, building extraction, road detection, and flood detection. Our method achieves an average of 5.8%, 8.2%, 4.0%, and 15.3% improvement over state-of-the-art methods on the 4 tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_SegEarth-OV_Towards_Training-Free_Open-Vocabulary_Segmentation_for_Remote_Sensing_Images_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_SegEarth-OV_Towards_Training-Free_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Kaiyu and Liu, Ruixun and Cao, Xiangyong and Bai, Xueru and Zhou, Feng and Meng, Deyu and Wang, Zhi},
    title     = {SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10545-10556}
}

raw_affiliation:
[{"affiliation": "School of Software Engineering, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "School of Computer Science and Technology, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "School of Mathematics and Statistics, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Ministry of Education Key Laboratory of Intelligent Networks and Network Security, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}]

affiliations:
["School of Software Engineering, Xi\u2019an Jiaotong University", "School of Computer Science and Technology, Xi\u2019an Jiaotong University", "School of Mathematics and Statistics, Xi\u2019an Jiaotong University", "Ministry of Education Key Laboratory of Intelligent Networks and Network Security, Xi\u2019an Jiaotong University", "Pengcheng Laboratory", "Xidian University"]


authors: Zilong Chen, Yikai Wang, Wenqiang Sun, Feng Wang, Yiwen Chen, Huaping Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5835-5848
cities: ["Beijing", "Beijing", "Hong Kong", "Singapore"]
countries: ["China", "China", "China", "Singapore"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_MeshGen_Generating_PBR_Textured_Mesh_with_Render-Enhanced_Auto-Encoder_and_Generative_CVPR_2025_paper.html

title:
MeshGen: Generating PBR Textured Mesh with Render-Enhanced Auto-Encoder and Generative Data Augmentation

abstract:
In this paper, we introduce MeshGen, an advanced image-to-3D pipeline that generates high-quality 3D meshes with detailed geometry and physically based rendering (PBR) textures. Addressing the challenges faced by existing 3D native diffusion models, such as suboptimal auto-encoder performance, limited controllability, poor generalization, and inconsistent image-based PBR texturing, MeshGen employs several key innovations to overcome these limitations. We pioneer a render-enhanced point-to-shape auto-encoder that compresses meshes into a compact latent space, by designing perceptual optimization with ray-based regularization. This ensures that the 3D shapes are accurately represented and reconstructed to preserve geometric details within the latent space. To address data scarcity and image-shape misalignment, we further propose geometric augmentation and generative rendering augmentation techniques, which enhance the model's controllability and generalization ability, allowing it to perform well even with limited public datasets. For texture generation, MeshGen employs a reference attention-based multi-view ControlNet for consistent appearance synthesis. This is further complemented by our multi-view PBR decomposer that estimates PBR components and a UV inpainter that fills invisible areas, ensuring a seamless and consistent texture across the 3D mesh. Our extensive experiments demonstrate that MeshGen largely outperforms previous methods in both shape and texture generation, setting a new standard for the quality of 3D meshes generated with PBR textures.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_MeshGen_Generating_PBR_Textured_Mesh_with_Render-Enhanced_Auto-Encoder_and_Generative_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_MeshGen_Generating_PBR_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Zilong and Wang, Yikai and Sun, Wenqiang and Wang, Feng and Chen, Yiwen and Liu, Huaping},
    title     = {MeshGen: Generating PBR Textured Mesh with Render-Enhanced Auto-Encoder and Generative Data Augmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5835-5848}
}

raw_affiliation:
[{"affiliation": "Beijing National Research Center for Information Science and Technology (BNRist), Department of Computer Science and Technology, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Beijing Normal University", "city": "Beijing", "country": "China"}, {"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "S-Lab, School of Computer Science and Engineering, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Beijing National Research Center for Information Science and Technology (BNRist), Department of Computer Science and Technology, Tsinghua University", "School of Artificial Intelligence, Beijing Normal University", "HKUST", "S-Lab, School of Computer Science and Engineering, Nanyang Technological University"]


title: GIFStream: 4D Gaussian-based Immersive Video with Feature Stream
authors: Hao Li, Sicheng Li, Xiang Gao, Abudouaihati Batuer, Lu Yu, Yiyi Liao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21761-21770
raw_affiliation: [{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]
affiliations: ["Zhejiang University"]
cities: ["Hangzhou"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_GIFStream_4D_Gaussian-based_Immersive_Video_with_Feature_Stream_CVPR_2025_paper.html

abstract:
Immersive video offers a 6-Dof-free viewing experience, potentially playing a key role in future video technology. Recently, 4D Gaussian Splatting has gained attention as an effective approach for immersive video due to its high rendering efficiency and quality, though maintaining quality with manageable storage remains challenging. To address this, we introduce GIFStream, a novel 4D Gaussian representation using a canonical space and a deformation field enhanced with time-dependent feature streams. These feature streams enable complex motion modeling and allow efficient compression by leveraging their motion-awareness and temporal correspondence. Additionally, we incorporate both temporal and spatial compression networks for end-to-end compression. Experimental results show that GIFStream delivers high-quality immersive video at 30 Mbps, with real-time rendering and fast decoding on an RTX 4090.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_GIFStream_4D_Gaussian-based_Immersive_Video_with_Feature_Stream_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_GIFStream_4D_Gaussian-based_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Hao and Li, Sicheng and Gao, Xiang and Batuer, Abudouaihati and Yu, Lu and Liao, Yiyi},
    title     = {GIFStream: 4D Gaussian-based Immersive Video with Feature Stream},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21761-21770}
}


title: DeClotH: Decomposable 3D Cloth and Human Body Reconstruction from a Single Image
authors: Hyeongjin Nam, Donghwan Kim, Jeongtaek Oh, Kyoung Mu Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5636-5645
affiliations: ["Dept. of ECE&ASRI, Seoul National University", "IPAI, Seoul National University", "KRAFTON"]
cities: ["Seoul", "Seoul", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nam_DeClotH_Decomposable_3D_Cloth_and_Human_Body_Reconstruction_from_a_CVPR_2025_paper.html

abstract:
Most existing methods of 3D clothed human reconstruction from a single image treat the clothed human as a single object without distinguishing between cloth and human body. In this regard, we present DeClotH, which separately reconstructs 3D cloth and human body from a single image. This task remains largely unexplored due to the extreme occlusion between cloth and the human body, making it challenging to infer accurate geometries and textures. Moreover, while recent 3D human reconstruction methods have achieved impressive results using text-to-image diffusion models, directly applying such an approach to this problem often leads to incorrect guidance, particularly in reconstructing 3D cloth. To address these challenges, we propose two core designs in our framework.   First, to alleviate the occlusion issue, we leverage 3D template models of cloth and human body as regularizations, which provide strong geometric priors to prevent erroneous reconstruction by the occlusion. Second, we introduce a cloth diffusion model specifically designed to provide contextual information about cloth appearance, thereby enhancing the reconstruction of 3D cloth. Qualitative and quantitative experiments demonstrate that our proposed approach is highly effective in reconstructing both 3D cloth and the human body.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nam_DeClotH_Decomposable_3D_Cloth_and_Human_Body_Reconstruction_from_a_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nam_DeClotH_Decomposable_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nam_2025_CVPR,
    author    = {Nam, Hyeongjin and Kim, Donghwan and Oh, Jeongtaek and Lee, Kyoung Mu},
    title     = {DeClotH: Decomposable 3D Cloth and Human Body Reconstruction from a Single Image},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5636-5645}
}

raw_affiliation:
[{"affiliation": "Dept. of ECE&ASRI, Seoul National University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "IPAI, Seoul National University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "KRAFTON", "city": "Seoul", "country": "South Korea"}]


title: Neuron: Learning Context-Aware Evolving Representations for Zero-Shot Skeleton Action Recognition
authors: Yang Chen, Jingcai Guo, Song Guo, Dacheng Tao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8721-8730
cities: ["Hong Kong", "Hong Kong", "Singapore"]
countries: ["Hong Kong SAR", "Hong Kong SAR", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Neuron_Learning_Context-Aware_Evolving_Representations_for_Zero-Shot_Skeleton_Action_Recognition_CVPR_2025_paper.html

abstract:
Zero-shot skeleton action recognition is a non-trivial task that requires robust unseen generalization with prior knowledge from only seen classes and shared semantics. Existing methods typically build the skeleton-semantics interactions by uncontrollable mappings and conspicuous representations, thereby can hardly capture the intricate and fine-grained relationship for effective cross-modal transferability. To address these issues, we propose a novel dyNamically Evolving dUal skeleton-semantic syneRgistic framework with the guidance of cOntext-aware side informatioN (dubbed Neuron), to explore more fine-grained cross-modal correspondence from micro to macro perspectives at both spatial and temporal levels, respectively. Concretely, 1) we first construct the spatial-temporal evolving micro-prototypes and integrate dynamic context-aware side information to capture the intricate and synergistic skeleton-semantic correlations step-by-step, progressively refining cross-model alignment; and 2) we introduce the spatial compression and temporal memory mechanisms to guide the growth of spatial-temporal micro-prototypes, enabling them to absorb structure-related spatial representations and regularity-dependent temporal patterns. Notably, such processes are analogous to the learning and growth of neurons, equipping the framework with the capacity to generalize to novel unseen action categories. Extensive experiments on various benchmark datasets demonstrated the superiority of the proposed method. Code is available at: https://github.com/cseeyangchen/Neuron.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Neuron_Learning_Context-Aware_Evolving_Representations_for_Zero-Shot_Skeleton_Action_Recognition_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Yang and Guo, Jingcai and Guo, Song and Tao, Dacheng},
    title     = {Neuron: Learning Context-Aware Evolving Representations for Zero-Shot Skeleton Action Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8721-8730}
}

raw_affiliation:
[{"affiliation": "Department of Computing, The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "Hong Kong SAR"}, {"affiliation": "Department of CSE, The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "Hong Kong SAR"}, {"affiliation": "College of Computing and Data Science, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Department of Computing, The Hong Kong Polytechnic University", "Department of CSE, The Hong Kong University of Science and Technology", "College of Computing and Data Science, Nanyang Technological University"]


authors: Mohamed Abdelsamad, Michael Ulrich, Claudius Glaeser, Abhinav Valada
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22234-22243
affiliations: ["Bosch Center for AI", "University of Freiburg"]
cities: ["Stuttgart", "Freiburg"]
countries: ["Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Abdelsamad_Multi-Scale_Neighborhood_Occupancy_Masked_Autoencoder_for_Self-Supervised_Learning_in_LiDAR_CVPR_2025_paper.html

title:
Multi-Scale Neighborhood Occupancy Masked Autoencoder for Self-Supervised Learning in LiDAR Point Clouds

abstract:
Masked autoencoders (MAE) have shown tremendous potential for self-supervised learning (SSL) in vision and beyond. However, point clouds from LiDARs used in automated driving are particularly challenging for MAEs since large areas of the 3D volume are empty. Consequently, existing work suffers from leaking occupancy information into the decoder and has significant computational complexity, thereby limiting the SSL pre-training to only 2D bird's eye view encoders in practice. In this work, we propose the novel neighborhood occupancy MAE (NOMAE) that overcomes the aforementioned challenges by employing masked occupancy reconstruction only in the neighborhood of non-masked voxels. We incorporate voxel masking and occupancy reconstruction at multiple scales with our proposed hierarchical mask generation technique to capture features of objects of different sizes in the point cloud. NOMAEs are extremely flexible and can be directly employed for SSL in existing 3D architectures. We perform extensive evaluations on the nuScenes and Waymo Open datasets for the downstream perception tasks of semantic segmentation and 3D object detection, comparing with both discriminative and generative SSL methods. The results demonstrate that NOMAE sets the new state-of-the-art on multiple benchmarks for multiple point cloud perception tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Abdelsamad_Multi-Scale_Neighborhood_Occupancy_Masked_Autoencoder_for_Self-Supervised_Learning_in_LiDAR_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Abdelsamad_Multi-Scale_Neighborhood_Occupancy_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Abdelsamad_2025_CVPR,
    author    = {Abdelsamad, Mohamed and Ulrich, Michael and Glaeser, Claudius and Valada, Abhinav},
    title     = {Multi-Scale Neighborhood Occupancy Masked Autoencoder for Self-Supervised Learning in LiDAR Point Clouds},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22234-22243}
}

raw_affiliation:
[{"affiliation": "Bosch Center for AI", "city": "Stuttgart", "country": "Germany"}, {"affiliation": "University of Freiburg", "city": "Freiburg", "country": "Germany"}]


title: Do We Really Need Curated Malicious Data for Safety Alignment in Multi-modal Large Language Models?
authors: Yanbo Wang, Jiyang Guan, Jian Liang, Ran He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19879-19889
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Do_We_Really_Need_Curated_Malicious_Data_for_Safety_Alignment_CVPR_2025_paper.html

abstract:
Multi-modal large language models (MLLMs) have made significant progress, yet their safety alignment remains limited. Typically, current open-source MLLMs rely on the alignment inherited from their language module to avoid harmful generations. However, the lack of safety measures specifically designed for multi-modal inputs creates an alignment gap, leaving MLLMs vulnerable to vision-domain attacks such as typographic manipulation. Current methods utilize a carefully designed safety dataset to enhance model defense capability.However, it is unknown what is actually learned in the high-quality dataset.Through comparison experiments, we find that the alignment gap primarily arises from data distribution biases, while image content, response quality, or the contrastive behavior of the dataset makes little contribution to boosting multi-modal safety. To further investigate this and identify the key factors in improving MLLM safety, we propose finetuning MLLMs on a small set of benign instruct-following data with responses replaced by simple, clear rejection sentences.Experiments show that, without the need for labor-intensive collection of high-quality malicious data, model safety can still be significantly improved, as long as a specific fraction of rejection data exists in the finetuning set, indicating the security alignment is not lost but rather obscured during multi-modal pretraining or instruction fine-tuning. Simply correcting the underlying data bias is enough to address the vision domain safety gap.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Do_We_Really_Need_Curated_Malicious_Data_for_Safety_Alignment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Do_We_Really_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yanbo and Guan, Jiyang and Liang, Jian and He, Ran},
    title     = {Do We Really Need Curated Malicious Data for Safety Alignment in Multi-modal Large Language Models?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19879-19889}
}

raw_affiliation:
[{"affiliation": "School of Artificial Intelligence, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "NLPR & MAIS, Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["School of Artificial Intelligence, University of Chinese Academy of Sciences", "NLPR & MAIS, Institute of Automation, Chinese Academy of Sciences"]


authors: Mingtao Guo, Guanyu Xing, Yanli Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 228-238
cities: ["Chengdu", "Chengdu", "Chengdu"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_High-Fidelity_Relightable_Monocular_Portrait_Animation_with_Lighting-Controllable_Video_Diffusion_Model_CVPR_2025_paper.html

title:
High-Fidelity Relightable Monocular Portrait Animation with Lighting-Controllable Video Diffusion Model

abstract:
Relightable portrait animation aims to animate a static reference portrait to match the head movements of a driving video while adapting to user-specified or reference lighting conditions. Existing portrait animation methods fail to achieve relightable portraits because they do not separate and manipulate intrinsic (identity and appearance) and extrinsic (pose and lighting) features. In this paper, we present a Lighting Controllable Video Diffusion model (LCVD) for high-fidelity, relightable portrait animation. We address this limitation by distinguishing these feature types through dedicated subspaces within the feature space of a pre-trained image-to-video diffusion model. Specifically, for each frame, we use the 3D mesh of the reference portrait, the pose of the driving frame as well as the specified lighting to render an image called shading hint. While the reference image represents the intrinsic attributes, the shading hint encodes the extrinsic attributes. In the training phase, we employ a reference adapter to map the reference into an intrinsic feature subspace and a shading adapter to map the shading hints into an extrinsic feature subspace. By merging features from these subspaces, the model achieves nuanced control over lighting and pose in generated animations. Extensive evaluations show that LCVD outperforms state-of-the-art methods in lighting realism, image quality, and video consistency, setting a new benchmark in relightable portrait animation. Our project is available at \href https://github.com/MingtaoGuo/Relightable-Portrait-Animation  https://github.com/MingtaoGuo/Relightable-Portrait-Animation

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_High-Fidelity_Relightable_Monocular_Portrait_Animation_with_Lighting-Controllable_Video_Diffusion_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_High-Fidelity_Relightable_Monocular_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Mingtao and Xing, Guanyu and Liu, Yanli},
    title     = {High-Fidelity Relightable Monocular Portrait Animation with Lighting-Controllable Video Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {228-238}
}

raw_affiliation:
[{"affiliation": "National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University", "city": "Chengdu", "country": "China"}, {"affiliation": "School of Cyber Science and Engineering, Sichuan University", "city": "Chengdu", "country": "China"}, {"affiliation": "College of Computer Science, Sichuan University", "city": "Chengdu", "country": "China"}]

affiliations:
["National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University", "School of Cyber Science and Engineering, Sichuan University", "College of Computer Science, Sichuan University"]


title: Plug-and-Play PPO: An Adaptive Point Prompt Optimizer Making SAM Greater
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4332-4342
affiliations: ["Taiyuan University of Technology", "University of Science and Technology of China"]
cities: ["Taiyuan", "Hefei"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Plug-and-Play_PPO_An_Adaptive_Point_Prompt_Optimizer_Making_SAM_Greater_CVPR_2025_paper.html

authors:
Xueyu Liu, Rui Wang, Yexin Lai, Guangze Shi, Feixue Shao, Fang Hao, Jianan Zhang, Jia Shen, Yongfei Wu, Wen Zheng

abstract:
Powered by extensive curated training data, the Segment Anything Model (SAM) demonstrates impressive generalization capabilities in open-world scenarios, effectively guided by user-provided prompts. However, the class-agnostic characteristic of SAM renders its segmentation accuracy highly dependent on prompt quality. In this paper, we propose a novel pplug-and-play dual-space Point Prompt Optimizer (PPO) designed to enhance prompt distribution through deep reinforcement learning (DRL)-based heterogeneous graph optimization. PPO optimizes initial prompts for any task without requiring additional training, thereby improving SAM's downstream segmentation performance. Specifically, PPO constructs a dual-space heterogeneous graph, leveraging the robust feature-matching capabilities of a foundational pre-trained model to create internal feature and physical distance matrices. A DRL policy network iteratively refines the distribution of prompt points, optimizing segmentation predictions. We conducted experiments on four public datasets. The ablation study explores the necessity and balance of optimizing prompts in both feature and physical spaces. The comparative study shows that PPO enables SAM to surpass recent one-shot methods. Additionally, experiments with different initial prompts demonstrate PPO's generality across prompts generated by various methods. In conclusion, PPO redefines the prompt optimization problem as a heterogeneous graph optimization task, using DRL to construct an effective, plug-and-play prompt optimizer. This approach holds potential for broader applications across diverse segmentation tasks and provides a promising solution for point prompt optimization. The source code and demo are available at https://github.com/XueyuLiu/PPO.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Plug-and-Play_PPO_An_Adaptive_Point_Prompt_Optimizer_Making_SAM_Greater_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Plug-and-Play_PPO_An_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Xueyu and Wang, Rui and Lai, Yexin and Shi, Guangze and Shao, Feixue and Hao, Fang and Zhang, Jianan and Shen, Jia and Wu, Yongfei and Zheng, Wen},
    title     = {Plug-and-Play PPO: An Adaptive Point Prompt Optimizer Making SAM Greater},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4332-4342}
}

raw_affiliation:
[{"affiliation": "Taiyuan University of Technology", "city": "Taiyuan", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}]


title: Harnessing Global-Local Collaborative Adversarial Perturbation for Anti-Customization
authors: Long Xu, Jiakai Wang, Haojie Hao, Haotong Qin, Jiejie Zhao, Xianglong Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13414-13423
affiliations: ["Beihang University", "Zhongguancun Laboratory", "ETH Z\u00a8urich"]
cities: ["Beijing", "Beijing", "Zurich"]
countries: ["China", "China", "Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Harnessing_Global-Local_Collaborative_Adversarial_Perturbation_for_Anti-Customization_CVPR_2025_paper.html

abstract:
Though achieving significant success in personalized image synthesis, Latent Diffusion Models (LDMs) pose substantial social risks caused by unauthorized misuse (e.g., face theft). To counter these threats, the Anti-Customization (AC) method that exploits adversarial perturbations was proposed. Unfortunately, existing AC methods show insufficient defense ability due to the ignorance of hierarchical characteristics, i.e., global feature correlations and local facial attributes, leading to weak resistance to concept transfer and semantic theft in customization methods. To address these limitations, we are motivated to propose a **G**lobal-L**o**cal c**o**llaborate**d** Anti-Customization (GoodAC) framework to generate powerful adversarial perturbations by disturbing both feature correlations and facial attributes. To enhance the ability to resist concept transfer, we disrupt the spatial correlation of perceptual features that form the basis of model generation at a global level, thereby creating highly concept-transfer-resistant adversarial camouflage. To improve the ability to resist semantic theft, leveraging the fact that facial attributes are personalized, we designed a personalized and precise facial attribute distortion strategy locally, focusing the attack on the individual's image structure to generate strong camouflage. Extensive experiments on various customization methods, including Dreambooth and LoRA, have strongly demonstrated that our GoodAC outperforms other state-of-the-art approaches by large margins, e.g., over 50% improvements on ISM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Harnessing_Global-Local_Collaborative_Adversarial_Perturbation_for_Anti-Customization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_Harnessing_Global-Local_Collaborative_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Long and Wang, Jiakai and Hao, Haojie and Qin, Haotong and Zhao, Jiejie and Liu, Xianglong},
    title     = {Harnessing Global-Local Collaborative Adversarial Perturbation for Anti-Customization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13414-13423}
}

raw_affiliation:
[{"affiliation": "Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Zhongguancun Laboratory", "city": "Beijing", "country": "China"}, {"affiliation": "ETH Z\u00a8urich", "city": "Zurich", "country": "Switzerland"}]


title: EchoONE: Segmenting Multiple Echocardiography Planes in One Model
authors: Jiongtong Hu, Wufeng Xue, Jun Cheng, Yingying Liu, Wei Zhuo, Dong Ni
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5207-5216
affiliations: ["Shenzhen University", "Shenzhen People\u2019s Hospital"]
cities: ["Shenzhen", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_EchoONE_Segmenting_Multiple_Echocardiography_Planes_in_One_Model_CVPR_2025_paper.html

abstract:
In clinical practice of echocardiography examinations, multiple planes containing the heart structures of different view are usually required in screening, diagnosis and treatment of cardiac disease. AI models for echocardiography have to be tailored for a specific plane due to the dramatic structure differences, thus resulting in repetition development and extra complexity. Effective solution for such a multi-plane segmentation (MPS) problem is highly demanded for medical images, yet has not been well investigated. In this paper, we propose a novel solution, EchoONE, for this problem with a SAM-based segmentation architecture, a prior-composable mask learning (PC-Mask) module for semantic-aware dense prompt generation, and a learnable CNN-branch with a simple yet effective local feature fusion and adaption (LFFA) module for SAM adapting. We extensively evaluated our method on multiple internal and external echocardiography datasets, and achieved consistently state-of-the-art performance for multi-source datasets with different heart planes. This is the first time that the MPS problem is solved in one model for echocardiography data. The code will be available at https://github.com/a2502503/EchoONE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_EchoONE_Segmenting_Multiple_Echocardiography_Planes_in_One_Model_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Hu_2025_CVPR,
    author    = {Hu, Jiongtong and Xue, Wufeng and Cheng, Jun and Liu, Yingying and Zhuo, Wei and Ni, Dong},
    title     = {EchoONE: Segmenting Multiple Echocardiography Planes in One Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5207-5216}
}

raw_affiliation:
[{"affiliation": "Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Shenzhen People\u2019s Hospital", "city": "Shenzhen", "country": "China"}]


authors: Kendong Liu, Zhiyu Zhu, Hui Liu, Junhui Hou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18031-18040
affiliations: ["City University of Hong Kong", "Saint Francis University"]
cities: ["Hong Kong", "Hong Kong"]
countries: ["Hong Kong", "Hong Kong"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Acc3D_Accelerating_Single_Image_to_3D_Diffusion_Models_via_Edge_CVPR_2025_paper.html

title:
Acc3D: Accelerating Single Image to 3D Diffusion Models via Edge Consistency Guided Score Distillation

abstract:
We present Acc3D to tackle the challenge of accelerating the diffusion process to generate 3D models from single images. To derive high-quality reconstructions through few-step inferences, we emphasize the critical issue of regularizing the learning of score function in states of random noise. To this end, we propose edge consistency, i.e., consistent predictions across the high signal-to-noise ratio region, to enhance a pre-trained diffusion model, enabling a distillation-based refinement of the endpoint score function. Building on those distilled diffusion models, we propose an adversarial augmentation strategy to further enrich the generation detail and boost overall generation quality. The two modules complement each other, mutually reinforcing to elevate generative performance. Extensive experiments demonstrate that our Acc3D not only achieves over a 20xincrease in computational efficiency but also yields notable quality improvements, compared to the state-of-the-arts.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Acc3D_Accelerating_Single_Image_to_3D_Diffusion_Models_via_Edge_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Acc3D_Accelerating_Single_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Kendong and Zhu, Zhiyu and Liu, Hui and Hou, Junhui},
    title     = {Acc3D: Accelerating Single Image to 3D Diffusion Models via Edge Consistency Guided Score Distillation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18031-18040}
}

raw_affiliation:
[{"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "Hong Kong"}, {"affiliation": "Saint Francis University", "city": "Hong Kong", "country": "Hong Kong"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7037-7047
cities: ["Hong Kong", "Shanghai", "Hong Kong", "Singapore", "Saarbr\u00fccken", "College Station"]
countries: ["China", "China", "China", "Singapore", "Germany", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_EasyHOI_Unleashing_the_Power_of_Large_Models_for_Reconstructing_Hand-Object_CVPR_2025_paper.html

title:
EasyHOI: Unleashing the Power of Large Models for Reconstructing Hand-Object Interactions in the Wild

authors:
Yumeng Liu, Xiaoxiao Long, Zemin Yang, Yuan Liu, Marc Habermann, Christian Theobalt, Yuexin Ma, Wenping Wang

abstract:
Our work aims to reconstruct hand-object interactions from a single-view image, which is a fundamental but ill-posed task.Unlike methods that reconstruct from videos, multi-view images, or predefined 3D templates, single-view reconstruction faces significant challenges due to inherent ambiguities and occlusions. These challenges are further amplified by the diverse nature of hand poses and the vast variety of object shapes and sizes. Our key insight is that current foundational models for segmentation, inpainting, and 3D reconstruction robustly generalize to in-the-wild images, which could provide strong visual and geometric priors for reconstructing hand-object interactions.Specifically, given a single image, we first design a novel pipeline to estimate the underlying hand pose and object shape using off-the-shelf large models. Furthermore, with the initial reconstruction, we employ a prior-guided optimization scheme, which optimizes hand pose to comply with 3D physical constraints and the 2D input image content.We perform experiments across several datasets and show that our method consistently outperforms baselines and faithfully reconstructs a diverse set of hand-object interactions. Here is the link of our project page: https://lym29.github.io/EasyHOI-page/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_EasyHOI_Unleashing_the_Power_of_Large_Models_for_Reconstructing_Hand-Object_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_EasyHOI_Unleashing_the_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yumeng and Long, Xiaoxiao and Yang, Zemin and Liu, Yuan and Habermann, Marc and Theobalt, Christian and Ma, Yuexin and Wang, Wenping},
    title     = {EasyHOI: Unleashing the Power of Large Models for Reconstructing Hand-Object Interactions in the Wild},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7037-7047}
}

raw_affiliation:
[{"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "ShanghaiTech University", "city": "Shanghai", "country": "China"}, {"affiliation": "Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Max Planck Institute for Informatics", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "Texas A&M University", "city": "College Station", "country": "USA"}]

affiliations:
["The University of Hong Kong", "ShanghaiTech University", "Hong Kong University of Science and Technology", "Nanyang Technological University", "Max Planck Institute for Informatics", "Texas A&M University"]


title: PLeaS - Merging Models with Permutations and Least Squares
authors: Anshul Nasery, Jonathan Hayase, Pang Wei Koh, Sewoong Oh
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30493-30502
affiliations: ["University of Washington", "Allen Institute for AI"]
cities: ["Seattle", "Seattle"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nasery_PLeaS_-_Merging_Models_with_Permutations_and_Least_Squares_CVPR_2025_paper.html

abstract:
The democratization of machine learning systems has made the process of fine-tuning accessible to practitioners, leading to a wide range of open-source models fine-tuned on specialized tasks and datasets. Recent work has proposed to merge such models to combine their functionalities. However, prior approaches are usually restricted to models that are fine-tuned from the same base model. Furthermore, the final merged model is typically required to be of the same size as the original models. In this work, we propose a new two-step algorithm to merge models---termed PLeaS---which relaxes these constraints.First, leveraging the Permutation symmetries inherent in the two models, PLeaS partially matches nodes in each layer by maximizing alignment. Next, PLeaS computes the weights of the merged model as a layer-wise Least Squares solution to minimize the approximation error between the features of the merged model and the permuted features of the original models. PLeaS allows a practitioner to merge two models sharing the same architecture into a single performant model of a desired size, even when the two original models are fine-tuned from different base models. We also demonstrate how our method can be extended to address a challenging scenario where no data is available from the fine-tuning domains. We demonstrate our method to merge ResNet and ViT models trained with shared and different label spaces, and show improvement over the state-of-the-art merging methods  of up to 15 percentage points for the same target compute while merging models trained on DomainNet and fine-grained classification tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nasery_PLeaS_-_Merging_Models_with_Permutations_and_Least_Squares_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nasery_PLeaS_-_Merging_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nasery_2025_CVPR,
    author    = {Nasery, Anshul and Hayase, Jonathan and Koh, Pang Wei and Oh, Sewoong},
    title     = {PLeaS - Merging Models with Permutations and Least Squares},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30493-30502}
}

raw_affiliation:
[{"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "Allen Institute for AI", "city": "Seattle", "country": "USA"}]


title: Incremental Object Keypoint Learning
authors: Mingfu Liang, Jiahuan Zhou, Xu Zou, Ying Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25399-25410
cities: ["Evanston", "Beijing", "Wuhan"]
countries: ["USA", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_Incremental_Object_Keypoint_Learning_CVPR_2025_paper.html

abstract:
Existing progress in object keypoint estimation primarily benefits from the conventional supervised learning paradigm based on numerous data labeled with pre-defined keypoints. However, these well-trained models can hardly detect the undefined new keypoints in test time, which largely hinders their feasibility for diverse downstream tasks. To handle this, various solutions are explored but still suffer from either limited generalizability or transferability. Therefore, in this paper, we explore a novel keypoint learning paradigm in that we only annotate new keypoints in the new data and incrementally train the model, without retaining any old data, called Incremental object Keypoint Learning (IKL). A two-stage learning scheme as a novel baseline tailored to IKL is developed. In the first Knowledge Association stage, given the data labeled with only new keypoints, an auxiliary KA-Net is trained to automatically associate the old keypoints to these new ones based on their spatial and intrinsic anatomical relations. In the second Mutual Promotion stage, based on a keypoint-oriented spatial distillation loss, we jointly leverage the auxiliary KA-Net and the old model for knowledge consolidation to mutually promote the estimation of all old and new keypoints. Owing to the investigation of the correlations between new and old keypoints, our proposed method can not just effectively mitigate the catastrophic forgetting of old keypoints, but may even further improve the estimation of the old ones and achieve a positive transfer beyond anti-forgetting. Such an observation has been solidly verified by extensive experiments on different keypoint datasets, where our method exhibits superiority in alleviating the forgetting issue and boosting performance while enjoying labeling efficiency even under the low-shot data regime.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_Incremental_Object_Keypoint_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_Incremental_Object_Keypoint_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Mingfu and Zhou, Jiahuan and Zou, Xu and Wu, Ying},
    title     = {Incremental Object Keypoint Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25399-25410}
}

raw_affiliation:
[{"affiliation": "Northwestern University", "city": "Evanston", "country": "USA"}, {"affiliation": "Wangxuan Institute of Computer Technology, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}]

affiliations:
["Northwestern University", "Wangxuan Institute of Computer Technology, Peking University", "Huazhong University of Science and Technology"]


title: Soft Self-labeling and Potts Relaxations for Weakly-supervised Segmentation
authors: Zhongwen Zhang, Yuri Boykov
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20244-20253
raw_affiliation: [{"affiliation": "University of Waterloo", "city": "Waterloo", "country": "Canada"}]
affiliations: ["University of Waterloo"]
cities: ["Waterloo"]
countries: ["Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Soft_Self-labeling_and_Potts_Relaxations_for_Weakly-supervised_Segmentation_CVPR_2025_paper.html

abstract:
We consider weakly supervised segmentation where only a fraction of pixels have ground truth labels (scribbles) and focus on a self-labeling approach optimizing relaxations of the standard unsupervised CRF/Potts loss on unlabeled pixels. While WSSS methods can directly optimize such losses via gradient descent, prior work suggests that higher-order optimization can improve network training by introducing hidden pseudo-labels and powerful CRF sub-problem solvers, e.g. graph cut. However, previously used hard pseudo-labels can not represent class uncertainty or errors, which motivates soft self-labeling. We derive a principled auxiliary loss and systematically evaluate standard and new CRF relaxations (convex and non-convex), neighborhood systems, and terms connecting network predictions with soft pseudo-labels. We also propose a general continuous sub-problem solver. Using only standard architectures, soft self-labeling consistently improves scribble-based training and outperforms significantly more complex specialized WSSS systems. It can outperform full pixel-precise supervision. Our general ideas apply to other weakly-supervised problems/systems.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Soft_Self-labeling_and_Potts_Relaxations_for_Weakly-supervised_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Soft_Self-labeling_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zhongwen and Boykov, Yuri},
    title     = {Soft Self-labeling and Potts Relaxations for Weakly-supervised Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20244-20253}
}


title: MVSAnywhere: Zero-Shot Multi-View Stereo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11493-11504
affiliations: ["Niantic", "University of Edinburgh", "I3A, Universidad de Zaragoza", "UCL"]
cities: ["San Francisco", "Edinburgh", "Zaragoza", "London"]
countries: ["USA", "UK", "Spain", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Izquierdo_MVSAnywhere_Zero-Shot_Multi-View_Stereo_CVPR_2025_paper.html

authors:
Sergio Izquierdo, Mohamed Sayed, Michael Firman, Guillermo Garcia-Hernando, Daniyar Turmukhambetov, Javier Civera, Oisin Mac Aodha, Gabriel Brostow, Jamie Watson

abstract:
Computing accurate depth from multiple views is a fundamental and longstanding challenge in computer vision.However, most existing approaches do not generalize well across different domains and scene types (e.g. indoor vs outdoor). Training a general-purpose multi-view stereo model is challenging and raises several questions, e.g. how to best make use of transformer-based architectures, how to incorporate additional metadata when there is a variable number of input views, and how to estimate the range of valid depths which can vary considerably across different scenes and is typically not known a priori? To address these issues, we introduce MVSA, a novel and versatile Multi-View Stereo architecture that aims to work Anywhere by generalizing across diverse domains and depth ranges. MVSA combines monocular and multi-view cues with an adaptive cost volume to deal with scale-related issues. We demonstrate state-of-the-art zero-shot depth estimation on the Robust Multi-View Depth Benchmark, surpassing  existing multi-view stereo and monocular baselines.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Izquierdo_MVSAnywhere_Zero-Shot_Multi-View_Stereo_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Izquierdo_2025_CVPR,
    author    = {Izquierdo, Sergio and Sayed, Mohamed and Firman, Michael and Garcia-Hernando, Guillermo and Turmukhambetov, Daniyar and Civera, Javier and Mac Aodha, Oisin and Brostow, Gabriel and Watson, Jamie},
    title     = {MVSAnywhere: Zero-Shot Multi-View Stereo},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11493-11504}
}

raw_affiliation:
[{"affiliation": "Niantic", "city": "San Francisco", "country": "USA"}, {"affiliation": "University of Edinburgh", "city": "Edinburgh", "country": "UK"}, {"affiliation": "I3A, Universidad de Zaragoza", "city": "Zaragoza", "country": "Spain"}, {"affiliation": "UCL", "city": "London", "country": "UK"}]


title: InteractVLM: 3D Interaction Reasoning from 2D Foundational Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22605-22615
cities: ["T\u00fcbingen", "Amsterdam", "Paris"]
countries: ["Germany", "the Netherlands", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dwivedi_InteractVLM_3D_Interaction_Reasoning_from_2D_Foundational_Models_CVPR_2025_paper.html

authors:
Sai Kumar Dwivedi, Dimitrije Antić, Shashank Tripathi, Omid Taheri, Cordelia Schmid, Michael J. Black, Dimitrios Tzionas

abstract:
We introduce InteractVLM, a novel method to estimate 3D contact points on human bodies and objects from single in-the-wild images, enabling accurate human-object joint reconstruction in 3D. This is challenging due to occlusions, depth ambiguities, and widely varying object shapes. Existing methods rely on 3D contact annotations collected via expensive motion-capture systems or tedious manual labeling, limiting scalability and generalization. To overcome this, InteractVLM harnesses the broad visual knowledge of large Vision-Language Models (VLMs), fine-tuned with limited 3D contact data. However, directly applying these models is non-trivial, as they reason only in 2D, while human-object contact is inherently 3D. Thus we introduce a novel Render-Localize-Lift module that: (1) embeds 3D body and object surfaces in 2D space via multi-view rendering, (2) trains a novel multi-view localization model (MV-Loc) to infer contacts in 2D, and (3) lifts these to 3D. Additionally, we propose a new task called Semantic Human Contact estimation, where human contact predictions are conditioned explicitly on object semantics, enabling richer interaction modeling. InteractVLM outperforms existing work on contact estimation and also facilitates 3D reconstruction from an in-the wild image.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dwivedi_InteractVLM_3D_Interaction_Reasoning_from_2D_Foundational_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dwivedi_InteractVLM_3D_Interaction_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dwivedi_2025_CVPR,
    author    = {Dwivedi, Sai Kumar and Anti\'c, Dimitrije and Tripathi, Shashank and Taheri, Omid and Schmid, Cordelia and Black, Michael J. and Tzionas, Dimitrios},
    title     = {InteractVLM: 3D Interaction Reasoning from 2D Foundational Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22605-22615}
}

raw_affiliation:
[{"affiliation": "Max Planck Institute for Intelligent Systems", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "University of Amsterdam (UvA)", "city": "Amsterdam", "country": "the Netherlands"}, {"affiliation": "Inria, \u00b4Ecole normale sup\u00b4erieure", "city": "Paris", "country": "France"}]

affiliations:
["Max Planck Institute for Intelligent Systems", "University of Amsterdam (UvA)", "Inria, \u00b4Ecole normale sup\u00b4erieure"]


title: Patch Matters: Training-free Fine-grained Image Caption Enhancement via Local Perception
authors: Ruotian Peng, Haiying He, Yake Wei, Yandong Wen, Di Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3963-3973
cities: ["Guangzhou", "Beijing", "Beijing", "Hangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Peng_Patch_Matters_Training-free_Fine-grained_Image_Caption_Enhancement_via_Local_Perception_CVPR_2025_paper.html

abstract:
High-quality image captions play a crucial role in improving the performance of cross-modal applications such as text-to-image generation, text-to-video generation, and text-image retrieval. To generate long-form, high-quality captions, many recent studies have employed multimodal large language models (MLLMs). However, current MLLMs often produce captions that lack fine-grained details or suffer from hallucinations, a challenge that persists in both open-source and closed-source models. Inspired by Feature-Integration theory, which suggests that attention must focus on specific regions to integrate visual information effectively, we propose a divide-then-aggregate strategy. Our method first divides the image into semantic and spatial patches to extract fine-grained details, enhancing the model's local perception of the image. These local details are then hierarchically aggregated to generate a comprehensive global description. To address hallucinations and inconsistencies in the generated captions, we apply a semantic-level filtering process during hierarchical aggregation. This training-free pipeline can be applied to both open-source models (LLaVA-1.5, LLaVA-1.6, Mini-Gemini) and closed-source models (Claude-3.5-Sonnet, GPT-4o, GLM-4V-Plus). Extensive experiments demonstrate that our method generates more detailed, reliable captions, advancing multimodal description generation without requiring model retraining.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Peng_Patch_Matters_Training-free_Fine-grained_Image_Caption_Enhancement_via_Local_Perception_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Peng_Patch_Matters_Training-free_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Peng_2025_CVPR,
    author    = {Peng, Ruotian and He, Haiying and Wei, Yake and Wen, Yandong and Hu, Di},
    title     = {Patch Matters: Training-free Fine-grained Image Caption Enhancement via Local Perception},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3963-3973}
}

raw_affiliation:
[{"affiliation": "School of Future Technology, South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "College of Science, China Agricultural University", "city": "Beijing", "country": "China"}, {"affiliation": "Gaoling School of Artificial Intelligence, Renmin University of China", "city": "Beijing", "country": "China"}, {"affiliation": "School of Engineering, Westlake University", "city": "Hangzhou", "country": "China"}]

affiliations:
["School of Future Technology, South China University of Technology", "College of Science, China Agricultural University", "Gaoling School of Artificial Intelligence, Renmin University of China", "School of Engineering, Westlake University"]


title: Attribute-Missing Multi-view Graph Clustering
authors: Bowen Zhao, Qianqian Wang, Zhengming Ding, Quanxue Gao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25832-25841
affiliations: ["Xidian University", "Tulane University"]
cities: ["Xi'an", "New Orleans"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Attribute-Missing_Multi-view_Graph_Clustering_CVPR_2025_paper.html

abstract:
The success of existing deep multi-view graph clustering methods is based on the assumption that node attributes are fully available across all views. However, in practical scenarios, node attributes are frequently missing due to factors such as data privacy concerns or failures in data collection devices. Although some methods have been proposed to address the issue of missing node attributes, they come with the following limitations: i) Existing methods are often not tailored specifically for clustering tasks and struggle to address missing attributes effectively. ii) They tend to ignore the relational dependencies between nodes and their neighboring nodes. This oversight results in unreliable imputations, thereby degrading clustering performance. To address the above issues, we propose an Attribute-Missing Multi-view Graph Clustering (AMMGC). Specifically, we first impute missing node attributes by leveraging neighborhood information through an adjacency matrix. Then, to improve the consistency, we integrate a dual structure consistency module that aligns graph structures across multiple views, reducing redundancy and retaining key information. Furthermore, we introduce a high-confidence guidance module to improve the reliability of clustering. Extensive experiment results showcase the effectiveness and superiority of our proposed method on multiple benchmark datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Attribute-Missing_Multi-view_Graph_Clustering_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Bowen and Wang, Qianqian and Ding, Zhengming and Gao, Quanxue},
    title     = {Attribute-Missing Multi-view Graph Clustering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25832-25841}
}

raw_affiliation:
[{"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Tulane University", "city": "New Orleans", "country": "USA"}]


title: Generating 6DoF Object Manipulation Trajectories from Action Description in Egocentric Vision
authors: Tomoya Yoshida, Shuhei Kurita, Taichi Nishimura, Shinsuke Mori
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17370-17382
affiliations: ["Kyoto University", "National Institute of Informatics", "Sony Interactive Entertainment"]
cities: ["Kyoto", "Tokyo", "Tokyo"]
countries: ["Japan", "Japan", "Japan"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yoshida_Generating_6DoF_Object_Manipulation_Trajectories_from_Action_Description_in_Egocentric_CVPR_2025_paper.html

abstract:
Learning to use tools or objects in common scenes, particularly handling them in various ways as instructed, is a key challenge for developing interactive robots. Training models to generate such manipulation trajectories requires a large and diverse collection of detailed manipulation demonstrations for various objects, which is nearly unfeasible to gather at scale. In this paper, we propose a framework that leverages large-scale ego- and exo-centric video datasets --- constructed globally with substantial effort --- of Exo-Ego4D to extract diverse manipulation trajectories at scale. From these extracted trajectories with the associated textual action description, we develop trajectory generation models based on visual and point cloud-based language models. In the recently proposed egocentric vision-based in-a-quality trajectory dataset of HOT3D, we confirmed that our models successfully generate valid object trajectories, establishing a training dataset and baseline models for the novel task of generating 6DoF manipulation trajectories from action descriptions in egocentric vision.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yoshida_Generating_6DoF_Object_Manipulation_Trajectories_from_Action_Description_in_Egocentric_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yoshida_Generating_6DoF_Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yoshida_2025_CVPR,
    author    = {Yoshida, Tomoya and Kurita, Shuhei and Nishimura, Taichi and Mori, Shinsuke},
    title     = {Generating 6DoF Object Manipulation Trajectories from Action Description in Egocentric Vision},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17370-17382}
}

raw_affiliation:
[{"affiliation": "Kyoto University", "city": "Kyoto", "country": "Japan"}, {"affiliation": "National Institute of Informatics", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Sony Interactive Entertainment", "city": "Tokyo", "country": "Japan"}]


title: Pose-Guided Temporal Enhancement for Robust Low-Resolution Hand Reconstruction
authors: Kaixin Fan, Pengfei Ren, Jingyu Wang, Haifeng Sun, Qi Qi, Zirui Zhuang, Jianxin Liao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22627-22637
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fan_Pose-Guided_Temporal_Enhancement_for_Robust_Low-Resolution_Hand_Reconstruction_CVPR_2025_paper.html

abstract:
3D hand reconstruction is essential in non-contact human-computer interaction applications, but existing methods struggle with low-resolution images, which occur in slightly distant interactive scenes. Leveraging temporal information can mitigate the limitations of individual low-resolution images that lack detailed appearance information, thereby enhancing the robustness and accuracy of hand reconstruction. Existing temporal methods typically use joint features to represent temporal information, avoiding interference from redundant background information. However, joint features excessively disregard the spatial context of visual features, limiting hand reconstruction accuracy. We propose to integrate temporal joint features with visual features to construct a robust low-resolution visual representation. We introduce Triplane Features, a dense representation with 3D spatial awareness, to bridge the gap between the joint features and visual features that are misaligned in terms of representation form and semantics. Triplane Features are obtained by orthogonally projecting the joint features, embedding hand structure information into the 3D spatial context. Furthermore, we compress the spatial information of the three planes into a 2D dense feature thourgh Spatial-Aware Fusion to enhance the visual features. By using enhanced visual features enriched with temporal information for hand reconstruction, our method achieves competitive performance at much lower resolutions compared to state-of-the-art methods operating at high resolution on DexYCB, HanCo and H2O.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fan_Pose-Guided_Temporal_Enhancement_for_Robust_Low-Resolution_Hand_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fan_Pose-Guided_Temporal_Enhancement_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fan_2025_CVPR,
    author    = {Fan, Kaixin and Ren, Pengfei and Wang, Jingyu and Sun, Haifeng and Qi, Qi and Zhuang, Zirui and Liao, Jianxin},
    title     = {Pose-Guided Temporal Enhancement for Robust Low-Resolution Hand Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22627-22637}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}]

affiliations:
["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications"]


title: ReDiffDet: Rotation-equivariant Diffusion Model for Oriented Object Detection
authors: Jiaqi Zhao, Zeyu Ding, Yong Zhou, Hancheng Zhu, Wen-Liang Du, Rui Yao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24429-24439
cities: ["Xuzhou", "Xuzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_ReDiffDet_Rotation-equivariant_Diffusion_Model_for_Oriented_Object_Detection_CVPR_2025_paper.html

abstract:
The diffusion model has been successfully applied to various detection tasks. However, it still faces several challenges when used for oriented object detection: objects that are arbitrarily rotated require the diffusion model to encode their orientation information; uncontrollable random boxes inaccurately locate objects with dense arrangements and extreme aspect ratios; oriented boxes result in the misalignment between them and image features. To overcome these limitations, we propose ReDiffDet, a framework that formulates oriented object detection as a rotation-equivariant denoising diffusion process. First, we represent an oriented box as a 2D Gaussian distribution, forming the basis of the denoising paradigm. The reverse process can be proven to be rotation-equivariant within this representation and model framework. Second, we design a conditional encoder with conditional boxes to prevent boxes from being randomly placed across the entire image. Third, we propose an aligned decoder for alignment between oriented boxes and image features. The extensive experiments demonstrate ReDiffDet achieves promising performance and significantly outperforms the diffusion-based baseline detector.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_ReDiffDet_Rotation-equivariant_Diffusion_Model_for_Oriented_Object_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_ReDiffDet_Rotation-equivariant_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Jiaqi and Ding, Zeyu and Zhou, Yong and Zhu, Hancheng and Du, Wen-Liang and Yao, Rui},
    title     = {ReDiffDet: Rotation-equivariant Diffusion Model for Oriented Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24429-24439}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, China University of Mining and Technology", "city": "Xuzhou", "country": "China"}, {"affiliation": "Mine Digitization Engineering Research Center of the Ministry of Education", "city": "Xuzhou", "country": "China"}]

affiliations:
["School of Computer Science and Technology, China University of Mining and Technology", "Mine Digitization Engineering Research Center of the Ministry of Education"]


authors: HsiaoYuan Hsu, Yuxin Peng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8117-8127
affiliations: ["Wangxuan Institute of Computer Technology, Peking University"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hsu_PosterO_Structuring_Layout_Trees_to_Enable_Language_Models_in_Generalized_CVPR_2025_paper.html

title:
PosterO: Structuring Layout Trees to Enable Language Models in Generalized Content-Aware Layout Generation

abstract:
In poster design, content-aware layout generation is crucial for automatically arranging visual-textual elements on the given image. With limited training data, existing work focused on image-centric enhancement. However, this neglects the diversity of layouts and fails to cope with shape-variant elements or diverse design intents in generalized settings. To this end, we proposed a layout-centric approach that leverages layout knowledge implicit in large language models (LLMs) to create posters for omnifarious purposes, hence the name PosterO. Specifically, it structures layouts from datasets as trees in SVG language by universal shape, design intent vectorization, and hierarchical node representation. Then, it applies LLMs during inference to predict new layout trees by in-context learning with intent-aligned example selection. After layout trees are generated, we can seamlessly realize them into poster designs by editing the chat with LLMs. Extensive experimental results have demonstrated that PosterO can generate visually appealing layouts for given images, achieving new state-of-the-art performance across various benchmarks. To further explore PosterO's abilities under the generalized settings, we built PStylish7, the first dataset with multi-purpose posters and various-shaped elements, further offering a challenging test for advanced research. Code and dataset are publicly available at Project page.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hsu_PosterO_Structuring_Layout_Trees_to_Enable_Language_Models_in_Generalized_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hsu_PosterO_Structuring_Layout_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hsu_2025_CVPR,
    author    = {Hsu, HsiaoYuan and Peng, Yuxin},
    title     = {PosterO: Structuring Layout Trees to Enable Language Models in Generalized Content-Aware Layout Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8117-8127}
}


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19724-19735
raw_affiliation: [{"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}]
affiliations: ["Stanford University"]
cities: ["Stanford"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lozano_BIOMEDICA_An_Open_Biomedical_Image-Caption_Archive_Dataset_and_Vision-Language_Models_CVPR_2025_paper.html

title:
BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and Vision-Language Models Derived from Scientific Literature

authors:
Alejandro Lozano, Min Woo Sun, James Burgess, Liangyu Chen, Jeffrey J. Nirschl, Jeffrey Gu, Ivan Lopez, Josiah Aklilu, Anita Rau, Austin Wolfgang Katzer, Yuhui Zhang, Collin Chiu, Xiaohan Wang, Alfred Seunghoon Song, Robert Tibshirani, Serena Yeung-Levy

abstract:
The development of vision-language models (VLMs) is driven by large-scale and diverse multi-modal datasets. However, progress toward generalist biomedical VLMs is limited by the lack of annotated, publicly accessible datasets across biology and medicine. Existing efforts are limited to narrow domains, missing the opportunity to leverage the full diversity of biomedical knowledge encoded in scientific literature. To address this gap, we introduce BIOMEDICA: a scalable, open-source framework to extract, annotate, and serialize the entirety of the PubMed Central Open Access subset into an easy-to-use, publicly accessible dataset. Our framework produces a comprehensive archive with over 24 million unique image-text pairs from over 6 million articles. Metadata and expert-guided annotations are additionally provided. We demonstrate the utility and accessibility of our resource by releasing BMC-CLIP, a suite of CLIP-style models continuously pre-trained on BIOMEDICA dataset via streaming (eliminating the need to download 27 TB of data locally). On average, our models achieve state-of-the-art performance across 40 tasks -- spanning pathology, radiology, ophthalmology, dermatology, surgery, molecular biology, parasitology, and cell biology -- excelling in zero-shot classification with  6.56% average improvement (as high as 29.8% and 17.5% gains in dermatology and ophthalmology, respectively) and stronger image-text retrieval while using 10x less compute. To foster reproducibility and collaboration, we release our codebase and dataset to the broader research community

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lozano_BIOMEDICA_An_Open_Biomedical_Image-Caption_Archive_Dataset_and_Vision-Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lozano_BIOMEDICA_An_Open_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lozano_2025_CVPR,
    author    = {Lozano, Alejandro and Sun, Min Woo and Burgess, James and Chen, Liangyu and Nirschl, Jeffrey J. and Gu, Jeffrey and Lopez, Ivan and Aklilu, Josiah and Rau, Anita and Katzer, Austin Wolfgang and Zhang, Yuhui and Chiu, Collin and Wang, Xiaohan and Song, Alfred Seunghoon and Tibshirani, Robert and Yeung-Levy, Serena},
    title     = {BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and Vision-Language Models Derived from Scientific Literature},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19724-19735}
}


title: Unlocking Generalization Power in LiDAR Point Cloud Registration
authors: Zhenxuan Zeng, Qiao Wu, Xiyu Zhang, Lin Yuanbo Wu, Pei An, Jiaqi Yang, Ji Wang, Peng Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22244-22253
cities: ["Xi'an", "Ningbo", "Swansea", "Wuhan"]
countries: ["China", "China", "United Kingdom", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zeng_Unlocking_Generalization_Power_in_LiDAR_Point_Cloud_Registration_CVPR_2025_paper.html

abstract:
In real-world environments, a LiDAR point cloud registration method with robust generalization capabilities (across varying distances and datasets) is crucial for ensuring safety in autonomous driving and other LiDAR-based applications. However, current methods fall short in achieving this level of generalization. To address these limitations, we propose UGP, a pruned framework designed to enhance generalization power for LiDAR point cloud registration. The core insight in UGP is the elimination of cross-attention mechanisms to improve generalization, allowing the network to concentrate on intra-frame feature extraction. Additionally, we introduce a progressive self-attention module to reduce ambiguity in large-scale scenes and integrate Bird's Eye View (BEV) features to incorporate semantic information about scene elements. Together, these enhancements significantly boost the network's generalization performance. We validated our approach through various generalization experiments in multiple outdoor scenes. In cross-distance generalization experiments on KITTI and nuScenes, UGP achieved state-of-the-art mean Registration Recall rates of 94.5% and 91.4%, respectively. In cross-dataset generalization from nuScenes to KITTI, UGP achieved a state-of-the-art mean Registration Recall of 90.9%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zeng_Unlocking_Generalization_Power_in_LiDAR_Point_Cloud_Registration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zeng_Unlocking_Generalization_Power_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zeng_2025_CVPR,
    author    = {Zeng, Zhenxuan and Wu, Qiao and Zhang, Xiyu and Wu, Lin Yuanbo and An, Pei and Yang, Jiaqi and Wang, Ji and Wang, Peng},
    title     = {Unlocking Generalization Power in LiDAR Point Cloud Registration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22244-22253}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Northwestern Polytechnical University, China.", "city": "Xi'an", "country": "China"}, {"affiliation": "Ningbo Institute, Northwestern Polytechnical University, China.", "city": "Ningbo", "country": "China"}, {"affiliation": "Department of Computer Science, Swansea University, United Kingdom.", "city": "Swansea", "country": "United Kingdom"}, {"affiliation": "HuaZhong University of Science and Technology, China.", "city": "Wuhan", "country": "China"}]

affiliations:
["School of Computer Science, Northwestern Polytechnical University, China.", "Ningbo Institute, Northwestern Polytechnical University, China.", "Department of Computer Science, Swansea University, United Kingdom.", "HuaZhong University of Science and Technology, China."]


title: Structure-Aware Correspondence Learning for Relative Pose Estimation
authors: Yihan Chen, Wenfei Yang, Huan Ren, Shifeng Zhang, Tianzhu Zhang, Feng Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11611-11621
cities: ["Hefei", "Hefei", "Shenzhen"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Structure-Aware_Correspondence_Learning_for_Relative_Pose_Estimation_CVPR_2025_paper.html

abstract:
Relative pose estimation provides a promising way for achieving object-agnostic pose estimation. Despite the success of existing 3D correspondence-based methods, the reliance on explicit  feature matching suffers from small overlaps in visible regions and  unreliable  feature estimation for invisible regions.  Inspired by humans' ability to  assemble two object parts that have small or no overlapping regions by considering object structure,  we propose a novel Structure-Aware Correspondence Learning method for Relative Pose Estimation, which consists of two key modules. First, a structure-aware keypoint extraction module is designed to locate a set of kepoints that can represent the structure of objects with different shapes and appearance, under the guidance of a keypoint based image reconstruction loss. Second, a structure-aware correspondence estimation module is designed to model the intra-image and inter-image relationships between keypoints to extract structure-aware features for correspondence estimation. By jointly leveraging these two modules,  the proposed method can naturally estimate 3D-3D correspondences for unseen objects without explicit feature matching for precise relative pose estimation. Experimental results on the CO3D, Objaverse and LineMOD datasets demonstrate that the proposed method significantly outperforms prior methods, i.e., with 5.7^\circ reduction in mean angular error on the CO3D dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Structure-Aware_Correspondence_Learning_for_Relative_Pose_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Structure-Aware_Correspondence_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Yihan and Yang, Wenfei and Ren, Huan and Zhang, Shifeng and Zhang, Tianzhu and Wu, Feng},
    title     = {Structure-Aware Correspondence Learning for Relative Pose Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11611-11621}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "National Key Laboratory of Deep Space Exploration, Deep Space Exploration Laboratory", "city": "Hefei", "country": "China"}, {"affiliation": "Sangfor Technologies", "city": "Shenzhen", "country": "China"}]

affiliations:
["University of Science and Technology of China", "National Key Laboratory of Deep Space Exploration, Deep Space Exploration Laboratory", "Sangfor Technologies"]


authors: Zixuan Hu, Yongxian Wei, Li Shen, Chun Yuan, Dacheng Tao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25026-25037
cities: ["Singapore", "Beijing", "Shenzhen"]
countries: ["Singapore", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_LoRA_Recycle_Unlocking_Tuning-Free_Few-Shot_Adaptability_in_Visual_Foundation_Models_CVPR_2025_paper.html

title:
LoRA Recycle: Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs

abstract:
Large Language Models (LLMs) such as ChatGPT demonstrate strong few-shot adaptability without requiring fine-tuning, positioning them ideal for data-limited and real-time applications. However, this adaptability has not yet been replicated in current Visual Foundation Models (VFMs), which require explicit fine-tuning with sufficient tuning data. Besides, the pretraining-finetuning paradigm has led to the surge of numerous task-specific modular components, such as Low-Rank Adaptation (LoRA). For the first time, we explore the potential of reusing diverse pre-tuned LoRAs without accessing their original training data, to achieve tuning-free few-shot adaptation in VFMs. Our framework, LoRA Recycle, distills a meta-LoRA from diverse pre-tuned LoRAs with a meta-learning objective, using synthetic data inversely generated from pre-tuned LoRAs themselves. The VFM, once equipped with the meta-LoRA, is empowered to solve new few-shot tasks in a single forward pass, akin to the in-context learning of LLMs. Additionally, we incorporate a double-efficient mechanism, accelerating the data-generation and meta-training process while maintaining or even improving performance. Extensive experiments across various few-shot classification benchmarks across both in- and cross-domain scenarios demonstrate the superiority of our framework. Code is available at https://github.com/Egg-Hu/LoRA-Recycle.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_LoRA_Recycle_Unlocking_Tuning-Free_Few-Shot_Adaptability_in_Visual_Foundation_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hu_LoRA_Recycle_Unlocking_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hu_2025_CVPR,
    author    = {Hu, Zixuan and Wei, Yongxian and Shen, Li and Yuan, Chun and Tao, Dacheng},
    title     = {LoRA Recycle: Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25026-25037}
}

raw_affiliation:
[{"affiliation": "Nanyang Technological University, Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Tsinghua University, China", "city": "Beijing", "country": "China"}, {"affiliation": "Shenzhen Campus of Sun Yat-sen University, China", "city": "Shenzhen", "country": "China"}]

affiliations:
["Nanyang Technological University, Singapore", "Tsinghua University, China", "Shenzhen Campus of Sun Yat-sen University, China"]


title: One2Any: One-Reference 6D Pose Estimation for Any Object
authors: Mengya Liu, Siyuan Li, Ajad Chhatkuli, Prune Truong, Luc Van Gool, Federico Tombari
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6457-6467
affiliations: ["ETH Zurich", "INSAIT, Sofia University \u201cSt. Kliment Ohridski\u201d", "Google", "TUM"]
cities: ["Zurich", "Sofia", "Unknown", "Munich"]
countries: ["Switzerland", "Bulgaria", "Unknown", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_One2Any_One-Reference_6D_Pose_Estimation_for_Any_Object_CVPR_2025_paper.html

abstract:
6D object pose estimation remains challenging for many applications due to dependencies on complete 3D models, multi-view images, or training limited to specific object categories. These requirements make generalization to novel objects difficult for which neither 3D models nor multi-view images may be available. To address this, we propose a novel method One2Any that estimates the relative 6-degrees of freedom (DOF) object pose using only a single reference-single query RGB-D image, without prior knowledge of its 3D model, multi-view data, or category constraints. We treat object pose estimation as an encoding-decoding process: first, we obtain a comprehensive Reference Object Pose Embedding (ROPE) that encodes an object's shape, orientation, and texture from a single reference view. Using this embedding, a U-Net-based pose decoding module produces Reference Object Coordinate (ROC) for new views, enabling fast and accurate pose estimation. This simple encoding-decoding framework allows our model to be trained on any pair-wise pose data, enabling large-scale training and demonstrating great scalability. Experiments on multiple benchmark datasets demonstrate that our model generalizes well to novel objects, achieving state-of-the-art accuracy and robustness even rivaling methods that require multi-view or CAD inputs, at a fraction of compute.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_One2Any_One-Reference_6D_Pose_Estimation_for_Any_Object_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_One2Any_One-Reference_6D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Mengya and Li, Siyuan and Chhatkuli, Ajad and Truong, Prune and Van Gool, Luc and Tombari, Federico},
    title     = {One2Any: One-Reference 6D Pose Estimation for Any Object},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6457-6467}
}

raw_affiliation:
[{"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "INSAIT, Sofia University \u201cSt. Kliment Ohridski\u201d", "city": "Sofia", "country": "Bulgaria"}, {"affiliation": "Google", "city": "Unknown", "country": "Unknown"}, {"affiliation": "TUM", "city": "Munich", "country": "Germany"}]


title: PyTorchGeoNodes: Enabling Differentiable Shape Programs for 3D Shape Reconstruction
authors: Sinisa Stekovic, Arslan Artykov, Stefan Ainetter, Mattia D'Urso, Friedrich Fraundorfer
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16283-16292
cities: ["Paris", "Graz"]
countries: ["France", "Austria"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Stekovic_PyTorchGeoNodes_Enabling_Differentiable_Shape_Programs_for_3D_Shape_Reconstruction_CVPR_2025_paper.html

abstract:
We propose PyTorchGeoNodes, a differentiable module for reconstructing 3D objects and their parameters from images using interpretable shape programs. Unlike traditional CAD model retrieval, shape programs allow reasoning about semantic parameters, editing, and a low memory footprint. Despite their potential, shape programs for 3D scene understanding have been largely overlooked. Our key contribution is enabling gradient-based optimization by parsing shape programs, or more precisely procedural models designed in Blender, into efficient PyTorch code. While there are many possible applications of our PyTochGeoNodes, we show that a combination of PyTorchGeoNodes with genetic algorithm is a method of choice to optimize both discrete and continuous shape program parameters for 3D reconstruction and understanding of 3D object parameters. Our modular framework can be further integrated with other reconstruction algorithms, and we demonstrate one such integration to enable procedural Gaussian splatting. Our experiments on the ScanNet dataset show that our method achieves accurate reconstructions while enabling, until now, unseen level of 3D scene understanding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Stekovic_PyTorchGeoNodes_Enabling_Differentiable_Shape_Programs_for_3D_Shape_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Stekovic_PyTorchGeoNodes_Enabling_Differentiable_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Stekovic_2025_CVPR,
    author    = {Stekovic, Sinisa and Artykov, Arslan and Ainetter, Stefan and D'Urso, Mattia and Fraundorfer, Friedrich},
    title     = {PyTorchGeoNodes: Enabling Differentiable Shape Programs for 3D Shape Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16283-16292}
}

raw_affiliation:
[{"affiliation": "LIGM, \u00b4Ecole des Ponts et Chaussees, IP Paris, CNRS, France", "city": "Paris", "country": "France"}, {"affiliation": "Inst. for Computer Graphics and Vision, Graz Univ. of Technology, Austria", "city": "Graz", "country": "Austria"}]

affiliations:
["LIGM, \u00b4Ecole des Ponts et Chaussees, IP Paris, CNRS, France", "Inst. for Computer Graphics and Vision, Graz Univ. of Technology, Austria"]


title: Contextual AD Narration with Interleaved Multimodal Sequence
authors: Hanlin Wang, Zhan Tong, Kecheng Zheng, Yujun Shen, Limin Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8372-8383
cities: ["Nanjing", "Leuven", "Hangzhou"]
countries: ["China", "Belgium", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Contextual_AD_Narration_with_Interleaved_Multimodal_Sequence_CVPR_2025_paper.html

abstract:
The Audio Description (AD) task aims to generate descriptions of visual elements for visually impaired individuals to help them access long-form video contents, like movie. With video feature, text, character bank and context information as inputs, the generated ADs are able to correspond to the characters by name and provide reasonable, contextual descriptions to help audience understand the storyline of movie. To achieve this goal, we propose to leverage pre-trained foundation models through a simple and unified framework to generate ADs with interleaved multimodal sequence as input, termed as Uni-AD. To enhance the alignment of features across various modalities with finer granularity, we introduce a simple and lightweight module that maps video features into the textual feature space. Moreover, we also propose a character-refinement module to provide more precise information by identifying the main characters who play more significant role in the video context. With these unique designs, we further incorporate contextual information and a contrastive loss into our architecture to generate more smooth and contextual ADs. Experiments on the MAD-eval dataset show that Uni-AD can achieve state-of-the-art performance on AD generation, which demonstrates the effectiveness of our approach. Our code is available at: https://github.com/ant-research/UniAD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Contextual_AD_Narration_with_Interleaved_Multimodal_Sequence_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Contextual_AD_Narration_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Hanlin and Tong, Zhan and Zheng, Kecheng and Shen, Yujun and Wang, Limin},
    title     = {Contextual AD Narration with Interleaved Multimodal Sequence},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8372-8383}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory for Novel Software Technology, Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "ESAT, KU Leuven", "city": "Leuven", "country": "Belgium"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}]

affiliations:
["State Key Laboratory for Novel Software Technology, Nanjing University", "ESAT, KU Leuven", "Ant Group"]


title: FIFA: Fine-grained Inter-frame Attention for Driver's Video Gaze Estimation
authors: Daosong Hu, Mingyue Cui, Kai Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18760-18769
cities: ["Guangzhou", "Guangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_FIFA_Fine-grained_Inter-frame_Attention_for_Drivers_Video_Gaze_Estimation_CVPR_2025_paper.html

abstract:
Gaze direction serves as a pivotal indicator for assessing the level of driver attention. While image-based gaze estimation has been extensively researched, there has been a recent shift towards capturing gaze direction from video sequences. This approach encounters notable challenges, including the comprehension of the dynamic pupil evolution across frames and the extraction of head pose information from a relatively static background. To surmount these challenges, we introduce a dual-stream deep learning framework that explicitly models the displacement changes of the pupil through a fine-grained inter-frame attention mechanism and generates weights to adjust gaze embeddings. This technique transforms the face into a set of distinct patches and employs cross-attention to ascertain the correlation between pixel displacements in various patches and adjacent frames, thereby tracking spatial dynamics within the sequence. Our method is validated using two publicly available driver gaze datasets, and the results indicate that it achieves state-of-the-art performance or is on par with the best outcomes while reducing the parameters.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_FIFA_Fine-grained_Inter-frame_Attention_for_Drivers_Video_Gaze_Estimation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Hu_2025_CVPR,
    author    = {Hu, Daosong and Cui, Mingyue and Huang, Kai},
    title     = {FIFA: Fine-grained Inter-frame Attention for Driver's Video Gaze Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18760-18769}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Sun Yat-sen University, China", "city": "Guangzhou", "country": "China"}, {"affiliation": "Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen University, China", "city": "Guangzhou", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Sun Yat-sen University, China", "Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen University, China"]


title: MNE-SLAM: Multi-Agent Neural SLAM for Mobile Robots
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1485-1494
affiliations: ["Shanghai Jiao Tong University", "Nanyang Technological University"]
cities: ["Shanghai", "Singapore"]
countries: ["China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Deng_MNE-SLAM_Multi-Agent_Neural_SLAM_for_Mobile_Robots_CVPR_2025_paper.html

authors:
Tianchen Deng, Guole Shen, Chen Xun, Shenghai Yuan, Tongxin Jin, Hongming Shen, Yanbo Wang, Jingchuan Wang, Hesheng Wang, Danwei Wang, Weidong Chen

abstract:
Neural implicit scene representations have recently shown promising results in dense visual SLAM. However, existing implicit SLAM algorithms are constrained to single-agent scenarios, and fall difficulty in large indoor scenes and long sequences. Existing multi-agent SLAM frameworks cannot meet the constraints of communication bandwidth. To this end, we propose the first distributed multi-agent collaborative SLAM framework with distributed mapping and camera tracking, joint scene representation, intra-to-inter loop closure, and multi-submap fusion. Specifically, our proposed distributed neural mapping and tracking framework only needs peer-to-peer communication, which can greatly improve multi-agent cooperation and communication efficiency. A novel intra-to-inter loop closure method is designed to achieve local (single-agent) and global (multi-agent) consistency. Furthermore, to the best of our knowledge, there is no real-world dataset for NeRF-based/GS-based SLAM that provides both continuous-time trajectories groundtruth and high-accuracy 3D meshes groundtruth. To this end, we propose the first real-world indoor neural slam (INS) dataset covering both single-agent and multi-agent scenarios, ranging from small room to large-scale scenes, with high-accuracy ground truth for both 3D mesh and continuous-time camera trajectory. This dataset can advance the development of the community. Experiments on various datasets demonstrate the superiority of the proposed method in both mapping, tracking, and communication. The dataset and code will be open-source on https://github.com/dtc111111/MNESLAM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Deng_MNE-SLAM_Multi-Agent_Neural_SLAM_for_Mobile_Robots_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Deng_MNE-SLAM_Multi-Agent_Neural_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Deng_2025_CVPR,
    author    = {Deng, Tianchen and Shen, Guole and Xun, Chen and Yuan, Shenghai and Jin, Tongxin and Shen, Hongming and Wang, Yanbo and Wang, Jingchuan and Wang, Hesheng and Wang, Danwei and Chen, Weidong},
    title     = {MNE-SLAM: Multi-Agent Neural SLAM for Mobile Robots},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1485-1494}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]


title: TensoFlow: Tensorial Flow-based Sampler for Inverse Rendering
authors: Chun Gu, Xiaofei Wei, Li Zhang, Xiatian Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 495-504
cities: ["Shanghai", "Shanghai", "Guildford"]
countries: ["China", "China", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gu_TensoFlow_Tensorial_Flow-based_Sampler_for_Inverse_Rendering_CVPR_2025_paper.html

abstract:
Inverse rendering aims to recover scene geometry, material properties, and lighting from multi-view images. Given the complexity of light-surface interactions, importance sampling is essential for the evaluation of the rendering equation, as it reduces variance and enhances the efficiency of Monte Carlo sampling. Existing inverse rendering methods typically use pre-defined non-learnable importance samplers in prior manually, struggling to effectively match the spatially and directionally varied integrand and resulting in high variance and suboptimal performance. To address this limitation, we propose the concept of learning a spatially and directionally aware importance sampler for the rendering equation to accurately and flexibly capture the unconstrained complexity of a typical scene. We further formulate TensoFlow, a generic approach for sampler learning in inverse rendering, enabling to closely match the integrand of the rendering equation spatially and directionally. Concretely, our sampler is parameterized by normalizing flows, allowing both directional sampling of incident light and probability density function (PDF) inference. To capture the characteristics of the sampler spatially, we learn a tensorial representation of the scene space, which imposes spatial conditions, together with reflected direction, leading to spatially and directionally aware sampling distributions. Our model can be optimized by minimizing the difference between the integrand and our normalizing flow. Extensive experiments validate the superiority of TensoFlow over prior alternatives on both synthetic and real-world benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gu_TensoFlow_Tensorial_Flow-based_Sampler_for_Inverse_Rendering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gu_TensoFlow_Tensorial_Flow-based_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gu_2025_CVPR,
    author    = {Gu, Chun and Wei, Xiaofei and Zhang, Li and Zhu, Xiatian},
    title     = {TensoFlow: Tensorial Flow-based Sampler for Inverse Rendering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {495-504}
}

raw_affiliation:
[{"affiliation": "School of Data Science, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Innovation Institute", "city": "Shanghai", "country": "China"}, {"affiliation": "University of Surrey", "city": "Guildford", "country": "UK"}]

affiliations:
["School of Data Science, Fudan University", "Shanghai Innovation Institute", "University of Surrey"]


authors: Chengyue Huang, Brisa Maneechotesuwan, Shivang Chopra, Zsolt Kira
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3909-3918
raw_affiliation: [{"affiliation": "Georgia Institute of Technology", "city": "Atlanta", "country": "USA"}]
affiliations: ["Georgia Institute of Technology"]
cities: ["Atlanta"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_FRAMES-VQA_Benchmarking_Fine-Tuning_Robustness_across_Multi-Modal_Shifts_in_Visual_Question_CVPR_2025_paper.html

title:
FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering

abstract:
Visual question answering (VQA) systems face significant challenges when adapting to real-world data shifts, especially in multi-modal contexts. While robust fine-tuning strategies are essential for maintaining performance across in-distribution (ID) and out-of-distribution (OOD) scenarios, current evaluation settings are primarily unimodal or  particular to some types of OOD, offering limited insight into the complexities of multi-modal contexts. In this work, we propose a new benchmark FRAMES-VQA (Fine-Tuning Robustness Across Multi-Modal Shifts in VQA) for evaluating robust fine-tuning for VQA tasks. We utilize ten existing VQA benchmarks, including VQAv2, IV-VQA, VQA-CP, OK-VQA and others, and categorize them into ID, near and far OOD datasets covering uni-modal, multi-modal and adversarial distribution shifts. We first conduct a comprehensive comparison of existing robust fine-tuning methods. We then quantify the distribution shifts by calculating the Mahalanobis distance using uni-modal and multi-modal embeddings extracted from various models. Further, we perform an extensive analysis to explore the interactions between uni- and multi-modal shifts as well as modality importance for ID and OOD samples. These analyses offer valuable guidance on developing more robust fine-tuning methods to handle multi-modal distribution shifts. The code is available at https://github.com/chengyuehuang511/FRAMES-VQA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_FRAMES-VQA_Benchmarking_Fine-Tuning_Robustness_across_Multi-Modal_Shifts_in_Visual_Question_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_FRAMES-VQA_Benchmarking_Fine-Tuning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Chengyue and Maneechotesuwan, Brisa and Chopra, Shivang and Kira, Zsolt},
    title     = {FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3909-3918}
}


title: Shape Abstraction via Marching Differentiable Support Functions
authors: Sunkyung Park, Jeongmin Lee, Dongjun Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16902-16911
raw_affiliation: [{"affiliation": "Shape", "city": "Unknown", "country": "Unknown"}]
affiliations: ["Shape"]
cities: ["Unknown"]
countries: ["Unknown"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Park_Shape_Abstraction_via_Marching_Differentiable_Support_Functions_CVPR_2025_paper.html

abstract:
Shape abstraction, simplifying shape representation into a set of primitives, is a fundamental topic in computer vision. The choice of primitives shapes the structure of world understanding, yet achieving both high abstraction accuracy and versatility remains challenging. In this paper, we introduce a novel framework for shape abstraction utilizing a differentiable support function (DSF), which offers unique advantages in representing a wide range of convex shapes with fewer parameters, providing smooth surface approximation and enabling differentiable contact features (gap, point, normal) essential for downstream applications involving contact-related problems. To tackle the associated optimization and combinatorial challenges, we introduce two techniques: differentiable shape parameterization and hyperplane-based marching to enhance accuracy and reduce DSF requirements. We validate our method through experiments demonstrating superior accuracy and efficiency, and showcase its applicability in tasks requiring differentiable contact information.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Park_Shape_Abstraction_via_Marching_Differentiable_Support_Functions_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_Shape_Abstraction_via_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Park_2025_CVPR,
    author    = {Park, Sunkyung and Lee, Jeongmin and Lee, Dongjun},
    title     = {Shape Abstraction via Marching Differentiable Support Functions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16902-16911}
}


title: LSceneLLM: Enhancing Large 3D Scene Understanding Using Adaptive Visual Preferences
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3761-3771
cities: ["Guangzhou", "Shenzhen", "Shenyang", "Amherst", "Guangzhou", "Chengdu"]
countries: ["China", "China", "China", "USA", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhi_LSceneLLM_Enhancing_Large_3D_Scene_Understanding_Using_Adaptive_Visual_Preferences_CVPR_2025_paper.html

authors:
Hongyan Zhi, Peihao Chen, Junyan Li, Shuailei Ma, Xinyu Sun, Tianhang Xiang, Yinjie Lei, Mingkui Tan, Chuang Gan

abstract:
Research on 3D Vision-Language Models (3D-VLMs) is gaining increasing attention, which is crucial for developing embodied AI within 3D scenes, such as visual navigation and embodied question answering. Due to the high density of visual features, especially in large 3D scenes, accurately locating task-relevant visual information is challenging. Existing works attempt to segment all objectsand consider their features as scene representations. However, these task-agnostic object features include much redundant information and missing details for the task-relevant area. To tackle these problems, we propose LSceneLLM, an adaptive framework that automatically identifies task-relevant areas by leveraging LLM's visual preference for different tasks, followed by a plug-and-play scene magnifier module to capture fine-grained details in focused areas. Specifically, a dense token selector examines the attention map of LLM to identify visual preferences for the instruction input. It then magnifies fine-grained details of the focusing area. An adaptive self-attention module is leveraged to fuse the coarse-grained and selected fine-grained visual information. To comprehensively evaluate the large scene understanding ability of 3D-VLMs, we further introduce a cross-room understanding benchmark, XR-Scene, which contains a series of large scene understanding tasks including XR-QA, XR-EmbodiedPlanning, and XR-SceneCaption. Experiments show that our method surpasses existing methods on both large scene understanding and existing scene understanding benchmarks. Plunging our scene magnifier module into the existing 3D-VLMs also brings significant improvement.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhi_LSceneLLM_Enhancing_Large_3D_Scene_Understanding_Using_Adaptive_Visual_Preferences_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhi_LSceneLLM_Enhancing_Large_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhi_2025_CVPR,
    author    = {Zhi, Hongyan and Chen, Peihao and Li, Junyan and Ma, Shuailei and Sun, Xinyu and Xiang, Tianhang and Lei, Yinjie and Tan, Mingkui and Gan, Chuang},
    title     = {LSceneLLM: Enhancing Large 3D Scene Understanding Using Adaptive Visual Preferences},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3761-3771}
}

raw_affiliation:
[{"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Tencent Robotics X", "city": "Shenzhen", "country": "China"}, {"affiliation": "Northeastern University", "city": "Shenyang", "country": "China"}, {"affiliation": "UMass Amherst", "city": "Amherst", "country": "USA"}, {"affiliation": "Pazhou Laboratory", "city": "Guangzhou", "country": "China"}, {"affiliation": "Sichuan University", "city": "Chengdu", "country": "China"}]

affiliations:
["South China University of Technology", "Tencent Robotics X", "Northeastern University", "UMass Amherst", "Pazhou Laboratory", "Sichuan University"]


title: Event Fields: Capturing Light Fields at High Speed, Resolution, and Dynamic Range
authors: Ziyuan Qu, Zihao Zou, Vivek Boominathan, Praneeth Chakravarthula, Adithya Pediredla
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26910-26920
affiliations: ["Dartmouth College", "UNC Chapel Hill", "Rice University", "UNC Chapel Hill", "Dartmouth College"]
cities: ["Hanover", "Chapel Hill", "Houston", "Chapel Hill", "Hanover"]
countries: ["USA", "USA", "USA", "USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qu_Event_Fields_Capturing_Light_Fields_at_High_Speed_Resolution_and_CVPR_2025_paper.html

abstract:
Event cameras, which feature pixels that independently respond to changes in brightness, are becoming increasingly popular in high-speed applications due to their lower latency, reduced bandwidth requirements, and enhanced dynamic range compared to traditional frame-based cameras. Numerous imaging and vision techniques have leveraged event cameras for high-speed scene understanding by capturing high-framerate, high-dynamic range videos, primarily utilizing the temporal advantages inherent to event cameras. Additionally, imaging and vision techniques have utilized the light field---a complementary dimension to temporal information---for enhanced scene understanding. In this work, we propose "Event Fields", a new approach that utilizes innovative optical designs for event cameras to capture light fields at high speed.  We develop the underlying mathematical framework for Event Fields and introduce two foundational frameworks to capture them practically: spatial multiplexing to capture temporal derivatives and temporal multiplexing to capture angular derivatives. To realize these, we design two complementary optical setups---one using a kaleidoscope for spatial multiplexing and another using a galvanometer for temporal multiplexing.  We evaluate the performance of both designs using a custom-built simulator and real hardware prototypes, showcasing their distinct benefits. Our event fields unlock the full advantages of typical light fields--like post-capture refocusing and depth estimation--now supercharged for high-speed and high-dynamic range scenes. This novel light-sensing paradigm opens doors to new applications in photography, robotics, and AR/VR, and presents fresh challenges in rendering and machine learning.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qu_Event_Fields_Capturing_Light_Fields_at_High_Speed_Resolution_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qu_Event_Fields_Capturing_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Qu_2025_CVPR,
    author    = {Qu, Ziyuan and Zou, Zihao and Boominathan, Vivek and Chakravarthula, Praneeth and Pediredla, Adithya},
    title     = {Event Fields: Capturing Light Fields at High Speed, Resolution, and Dynamic Range},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26910-26920}
}

raw_affiliation:
[{"affiliation": "Dartmouth College", "city": "Hanover", "country": "USA"}, {"affiliation": "UNC Chapel Hill", "city": "Chapel Hill", "country": "USA"}, {"affiliation": "Rice University", "city": "Houston", "country": "USA"}, {"affiliation": "UNC Chapel Hill", "city": "Chapel Hill", "country": "USA"}, {"affiliation": "Dartmouth College", "city": "Hanover", "country": "USA"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23048-23058
affiliations: ["Wuhan University", "Seoul National University"]
cities: ["Wuhan", "Seoul"]
countries: ["China", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_HyperFree_A_Channel-adaptive_and_Tuning-free_Foundation_Model_for_Hyperspectral_Remote_CVPR_2025_paper.html

title:
HyperFree: A Channel-adaptive and Tuning-free Foundation Model for Hyperspectral Remote Sensing Imagery

authors:
Jingtao Li, Yingyi Liu, Xinyu Wang, Yunning Peng, Chen Sun, Shaoyu Wang, Zhendong Sun, Tian Ke, Xiao Jiang, Tangwei Lu, Anran Zhao, Yanfei Zhong

abstract:
Advanced interpretation of hyperspectral remote sensing images benefits many precise Earth observation tasks. Recently, visual foundation models have promoted the remote sensing interpretation but concentrating on RGB and multispectral images. Due to the varied hyperspectral channels, existing foundation models would face image-by-image tuning situation, imposing great pressure on hardware and time resources. In this paper, we propose a tuning-free hyperspectral foundation model called HyperFree, by adapting the existing visual prompt engineering. To process varied channel numbers, we design a learned weight dictionary covering full-spectrum from 0.4 2.5um, supporting to build the embedding layer dynamically. To make the prompt design more tractable, HyperFree can generate multiple semantic-aware masks for one prompt by treating feature distance as semantic-similarity. After pre-training HyperFree on constructed large-scale high-resolution hyperspectral images, HyperFree (1 prompt) has shown comparable results with specialized models (5 shots) on 5 tasks and 11 datasets. Code and dataset would be accessible at https://rsidea.whu.edu.cn/hyperfree.htm#.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_HyperFree_A_Channel-adaptive_and_Tuning-free_Foundation_Model_for_Hyperspectral_Remote_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_HyperFree_A_Channel-adaptive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Jingtao and Liu, Yingyi and Wang, Xinyu and Peng, Yunning and Sun, Chen and Wang, Shaoyu and Sun, Zhendong and Ke, Tian and Jiang, Xiao and Lu, Tangwei and Zhao, Anran and Zhong, Yanfei},
    title     = {HyperFree: A Channel-adaptive and Tuning-free Foundation Model for Hyperspectral Remote Sensing Imagery},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23048-23058}
}

raw_affiliation:
[{"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Seoul National University", "city": "Seoul", "country": "South Korea"}]


title: Exploring Temporally-Aware Features for Point Tracking
authors: Inès Hyeonsu Kim, Seokju Cho, Jiahui Huang, Jung Yi, Joon-Young Lee, Seungryong Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1962-1972
affiliations: ["KAIST AI", "Adobe Research"]
cities: ["Daejeon", "San Jose"]
countries: ["South Korea", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Exploring_Temporally-Aware_Features_for_Point_Tracking_CVPR_2025_paper.html

abstract:
Point tracking in videos is a fundamental task with applications in robotics, video editing, and more. While many vision tasks benefit from pre-trained feature backbones to improve generalizability, point tracking has primarily relied on simpler backbones trained from scratch on synthetic data, which may limit robustness in real-world scenarios. Additionally, point tracking requires temporal awareness to ensure coherence across frames, but using temporally-aware features is still underexplored. Most current methods often employ a two-stage process: an initial coarse prediction followed by a refinement stage to inject temporal information and correct errors from the coarse stage. These approach, however, is computationally expensive and potentially redundant if the feature backbone itself captures sufficient temporal information. In this work, we introduce Chrono, a feature backbone specifically designed for point tracking with built-in temporal awareness. Leveraging pre-trained representations from self-supervised learner DINOv2 and enhanced with a temporal adapter, Chrono effectively captures long-term temporal context, enabling precise prediction even without the refinement stage. Experimental results demonstrate that Chrono achieves state-of-the-art performance in a refiner-free setting on the TAP-Vid-DAVIS and TAP-Vid-Kinetics datasets, among common feature backbones used in point tracking as well as DINOv2, with exceptional efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Exploring_Temporally-Aware_Features_for_Point_Tracking_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, In\`es Hyeonsu and Cho, Seokju and Huang, Jiahui and Yi, Jung and Lee, Joon-Young and Kim, Seungryong},
    title     = {Exploring Temporally-Aware Features for Point Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1962-1972}
}

raw_affiliation:
[{"affiliation": "KAIST AI", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}]


authors: Dušan Malić, Christian Fruhwirth-Reisinger, Samuel Schulter, Horst Possegger
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27357-27367
cities: ["Graz", "Graz", "Unknown"]
countries: ["Austria", "Austria", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Malic_GBlobs_Explicit_Local_Structure_via_Gaussian_Blobs_for_Improved_Cross-Domain_CVPR_2025_paper.html

title:
GBlobs: Explicit Local Structure via Gaussian Blobs for Improved Cross-Domain LiDAR-based 3D Object Detection

abstract:
LiDAR-based 3D detectors need large datasets for training, yet they struggle to generalize to novel domains. Domain Generalization (DG) aims to mitigate this by training detectors that are invariant to such domain shifts. Current DG approaches exclusively rely on global geometric features (point cloud Cartesian coordinates) as input features. Over-reliance on these global geometric features can, however, cause 3D detectors to prioritize object location and absolute position, resulting in poor cross-domain performance. To mitigate this, we propose to exploit explicit local point cloud structure for DG, in particular by encoding point cloud neighborhoods with Gaussian blobs, GBlobs. Our proposed formulation is highly efficient and requires no additional parameters. Without any bells and whistles, simply by integrating GBlobs in existing detectors, we beat the current state-of-the-art in challenging single-source DG benchmarks by over 21 mAP (Waymo->KITTI), 13 mAP (KITTI->Waymo), and 12 mAP (nuScenes->KITTI), without sacrificing in-domain performance. Additionally, GBlobs demonstrate exceptional performance in multi-source DG, surpassing the current state-of-the-art by 17, 12, and 5 mAP on Waymo, KITTI, and ONCE, respectively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Malic_GBlobs_Explicit_Local_Structure_via_Gaussian_Blobs_for_Improved_Cross-Domain_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Malic_GBlobs_Explicit_Local_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Malic_2025_CVPR,
    author    = {Mali\'c, Du\v{s}an and Fruhwirth-Reisinger, Christian and Schulter, Samuel and Possegger, Horst},
    title     = {GBlobs: Explicit Local Structure via Gaussian Blobs for Improved Cross-Domain LiDAR-based 3D Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27357-27367}
}

raw_affiliation:
[{"affiliation": "Christian Doppler Laboratory for Embedded Machine Learning", "city": "Graz", "country": "Austria"}, {"affiliation": "Institute of Visual Computing, Graz University of Technology", "city": "Graz", "country": "Austria"}, {"affiliation": "Amazon", "city": "Unknown", "country": "Unknown"}]

affiliations:
["Christian Doppler Laboratory for Embedded Machine Learning", "Institute of Visual Computing, Graz University of Technology", "Amazon"]


title: V^2Dial: Unification of Video and Visual Dialog via Multimodal Experts
authors: Adnen Abdessaied, Anna Rohrbach, Marcus Rohrbach, Andreas Bulling
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8637-8647
affiliations: ["University of Stuttgart, Germany", "TU Darmstadt, Germany", "hessian.AI, Germany"]
cities: ["Stuttgart", "Darmstadt", "Germany"]
countries: ["Germany", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Abdessaied_V2Dial_Unification_of_Video_and_Visual_Dialog_via_Multimodal_Experts_CVPR_2025_paper.html

abstract:
We present V2Dial - a novel expert-based model specifically geared towards simultaneously handling image and video input data for multimodal conversational tasks. Current multimodal models primarily focus on simpler tasks (e.g., VQA, VideoQA, video-text retrieval) and often neglect the more challenging conversational counterparts, such as video and visual/image dialog. Moreover, works on both conversational tasks evolved separately from each other despite their apparent similarities limiting their applicability potential. To this end, we propose to unify both tasks using a single model that for the first time jointly learns the spatial and temporal features of images and videos by routing them through dedicated experts and aligns them using matching and contrastive learning techniques. Furthermore, we systemically study the domain shift between the two tasks by investigating whether and to what extent these seemingly related tasks can mutually benefit from their respective training data. Extensive evaluations on the widely used video and visual dialog datasets of AVSD and VisDial show that our model achieves new state-of-the-art results across four benchmarks both in zero-shot and fine-tuning settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Abdessaied_V2Dial_Unification_of_Video_and_Visual_Dialog_via_Multimodal_Experts_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Abdessaied_V2Dial_Unification_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Abdessaied_2025_CVPR,
    author    = {Abdessaied, Adnen and Rohrbach, Anna and Rohrbach, Marcus and Bulling, Andreas},
    title     = {V{\textasciicircum}2Dial: Unification of Video and Visual Dialog via Multimodal Experts},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8637-8647}
}

raw_affiliation:
[{"affiliation": "University of Stuttgart, Germany", "city": "Stuttgart", "country": "Germany"}, {"affiliation": "TU Darmstadt, Germany", "city": "Darmstadt", "country": "Germany"}, {"affiliation": "hessian.AI, Germany", "city": "Germany", "country": "Germany"}]


title: Detail-Preserving Latent Diffusion for Stable Shadow Removal
authors: Jiamin Xu, Yuxin Zheng, Zelong Li, Chi Wang, Renshu Gu, Weiwei Xu, Gang Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7592-7602
affiliations: ["Hangzhou Dianzi University", "Zhejiang University"]
cities: ["Hangzhou", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Detail-Preserving_Latent_Diffusion_for_Stable_Shadow_Removal_CVPR_2025_paper.html

abstract:
Achieving high-quality shadow removal with strong generalizability is challenging in scenes with complex global illumination. Due to the limited diversity in shadow removal datasets, current methods are prone to overfitting training data, often leading to reduced performance on unseen cases. To address this, we leverage the rich visual priors of a pre-trained Stable Diffusion (SD) model and propose a two-stage fine-tuning pipeline to adapt the SD model for stable and efficient shadow removal. In the first stage, we fix the VAE and fine-tune the denoiser in latent space, which yields substantial shadow removal but may lose some high-frequency details. To resolve this, we introduce a second stage, called the detail injection stage. This stage selectively extracts features from the VAE encoder to modulate the decoder, injecting fine details into the final results. Experimental results show that our method outperforms state-of-the-art shadow removal techniques. The cross-dataset evaluation further demonstrates that our method generalizes effectively to unseen data, enhancing the applicability of shadow removal methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Detail-Preserving_Latent_Diffusion_for_Stable_Shadow_Removal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_Detail-Preserving_Latent_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Jiamin and Zheng, Yuxin and Li, Zelong and Wang, Chi and Gu, Renshu and Xu, Weiwei and Xu, Gang},
    title     = {Detail-Preserving Latent Diffusion for Stable Shadow Removal},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7592-7602}
}

raw_affiliation:
[{"affiliation": "Hangzhou Dianzi University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]


title: Scaling Down Text Encoders of Text-to-Image Diffusion Models
authors: Lifu Wang, Daqing Liu, Xinchen Liu, Xiaodong He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18424-18433
affiliations: ["JD Explore Academy, JD.com Inc.", "Georgia Institute of Technology"]
cities: ["Beijing", "Atlanta"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Scaling_Down_Text_Encoders_of_Text-to-Image_Diffusion_Models_CVPR_2025_paper.html

abstract:
Text encoders in diffusion models have rapidly evolved, transitioning from CLIP to T5-XXL. Although this evolution has significantly enhanced the models' ability to understand complex prompts and generate text, it also leads to a substantial increase in the number of parameters. Despite T5 series encoders being trained on the C4 natural language corpus, which includes a significant amount of non-visual data, diffusion models with T5 encoder do not respond to those non-visual prompts, indicating redundancy in representational power. Therefore, it raises an important question: "Do we really need such a large text encoder?" In pursuit of an answer, we employ vision-based knowledge distillation to train a series of T5 encoder models. To fully inherit its capabilities, we constructed our dataset based on three criteria: image quality, semantic understanding, and text-rendering. Our results demonstrate the scaling down pattern that the distilled T5-base model can generate images of comparable quality to those produced by T5-XXL, while being 50 times smaller in size. This reduction in model size significantly lowers the GPU requirements for running state-of-the-art models such as FLUX and SD3, making high-quality text-to-image generation more accessible.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Scaling_Down_Text_Encoders_of_Text-to-Image_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Scaling_Down_Text_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Lifu and Liu, Daqing and Liu, Xinchen and He, Xiaodong},
    title     = {Scaling Down Text Encoders of Text-to-Image Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18424-18433}
}

raw_affiliation:
[{"affiliation": "JD Explore Academy, JD.com Inc.", "city": "Beijing", "country": "China"}, {"affiliation": "Georgia Institute of Technology", "city": "Atlanta", "country": "USA"}]


title: 3D Gaussian Head Avatars with Expressive Dynamic Appearances by Compact Tensorial Representations
authors: Yating Wang, Xuan Wang, Ran Yi, Yanbo Fan, Jichen Hu, Jingcheng Zhu, Lizhuang Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21117-21126
affiliations: ["Shanghai Jiao Tong University", "AntGroup Research"]
cities: ["Shanghai", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_3D_Gaussian_Head_Avatars_with_Expressive_Dynamic_Appearances_by_Compact_CVPR_2025_paper.html

abstract:
Recent studies have combined 3D Gaussian and 3D Morphable Models (3DMM) to construct high-quality 3D head avatars. In this line of research, existing methods either fail to capture the dynamic textures or incur significant overhead in terms of runtime speed or storage space. To this end, we propose a novel method that addresses all the aforementioned demands. In specific, we introduce an expressive and compact representation that encodes texture-related attributes of the 3D Gaussians in the tensorial format. We store appearance of neutral expression in static tri-planes, and represents dynamic texture details for different expressions using lightweight 1D feature lines, which are then decoded into opacity offset relative to the neutral face. We further propose adaptive truncated opacity penalty and class-balanced sampling to improve generalization across different expressions. Experiments show this design enables accurate face dynamic details capturing while maintains real-time rendering and significantly reduces storage costs, thus broadening the applicability to more scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_3D_Gaussian_Head_Avatars_with_Expressive_Dynamic_Appearances_by_Compact_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_3D_Gaussian_Head_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yating and Wang, Xuan and Yi, Ran and Fan, Yanbo and Hu, Jichen and Zhu, Jingcheng and Ma, Lizhuang},
    title     = {3D Gaussian Head Avatars with Expressive Dynamic Appearances by Compact Tensorial Representations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21117-21126}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "AntGroup Research", "city": "Hangzhou", "country": "China"}]


title: MambaIRv2: Attentive State Space Restoration
authors: Hang Guo, Yong Guo, Yaohua Zha, Yulun Zhang, Wenbo Li, Tao Dai, Shu-Tao Xia, Yawei Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28124-28133
cities: ["Beijing", "Saarbr\u00fccken", "Shanghai", "Hong Kong", "Shenzhen", "Shenzhen", "Z\u00fcrich"]
countries: ["China", "Germany", "China", "China", "China", "China", "Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_MambaIRv2_Attentive_State_Space_Restoration_CVPR_2025_paper.html

abstract:
The Mamba-based image restoration backbones have recently demonstrated significant potential in balancing global reception and computational efficiency. However, the inherent causal modeling limitation of Mamba, where each token depends solely on its predecessors in the scanned sequence, restricts the full utilization of pixels across the image and thus presents new challenges in image restoration. In this work, we propose MambaIRv2, which equips Mamba with the non-causal modeling ability similar to ViTs to reach the attentive state space restoration model. Specifically, the proposed attentive state-space equation allows to attend beyond the scanned sequence and facilitate image unfolding with just one single scan. Moreover, we further introduce a semantic-guided neighboring mechanism to encourage interaction between distant but similar pixels. Extensive experiments show our MambaIRv2 outperforms SRFormer by even 0.35dB PSNR for lightweight SR even with 9.3% less parameters and suppresses HAT on classic SR by up to 0.29dB.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_MambaIRv2_Attentive_State_Space_Restoration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_MambaIRv2_Attentive_State_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Hang and Guo, Yong and Zha, Yaohua and Zhang, Yulun and Li, Wenbo and Dai, Tao and Xia, Shu-Tao and Li, Yawei},
    title     = {MambaIRv2: Attentive State Space Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28124-28133}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Max Planck Institute for Informatics", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "ETH Z\u00a8urich", "city": "Z\u00fcrich", "country": "Switzerland"}]

affiliations:
["Tsinghua University", "Max Planck Institute for Informatics", "Shanghai Jiao Tong University", "The Chinese University of Hong Kong", "Shenzhen University", "Peng Cheng Laboratory", "ETH Z\u00a8urich"]


title: Floating No More: Object-Ground Reconstruction from a Single Image
authors: Yunze Man, Yichen Sheng, Jianming Zhang, Liang-Yan Gui, Yu-Xiong Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27134-27143
affiliations: ["University of Illinois Urbana-Champaign", "Purdue University", "Adobe"]
cities: ["Urbana-Champaign", "West Lafayette", "San Jose"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Man_Floating_No_More_Object-Ground_Reconstruction_from_a_Single_Image_CVPR_2025_paper.html

abstract:
Recent advancements in 3D object reconstruction from single images have primarily focused on improving the accuracy of object shapes. Yet, these techniques often fail to accurately capture the inter-relation between the object, ground, and camera. As a result, the reconstructed objects often appear floating or tilted when placed on flat surfaces. This limitation significantly affects 3D-aware image editing applications like shadow rendering and object pose manipulation. To address this issue, we introduce ORG (Object Reconstruction with Ground), a novel task aimed at reconstructing 3D object geometry in conjunction with the ground surface. Our method uses two compact pixel-level representations to depict the relationship between camera, object, and ground. Experiments show that the proposed ORG model can effectively reconstruct object-ground geometry on unseen data, significantly enhancing the quality of shadow generation and pose manipulation compared to conventional single-image 3D reconstruction techniques.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Man_Floating_No_More_Object-Ground_Reconstruction_from_a_Single_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Man_Floating_No_More_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Man_2025_CVPR,
    author    = {Man, Yunze and Sheng, Yichen and Zhang, Jianming and Gui, Liang-Yan and Wang, Yu-Xiong},
    title     = {Floating No More: Object-Ground Reconstruction from a Single Image},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27134-27143}
}

raw_affiliation:
[{"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana-Champaign", "country": "USA"}, {"affiliation": "Purdue University", "city": "West Lafayette", "country": "USA"}, {"affiliation": "Adobe", "city": "San Jose", "country": "USA"}]


title: POT: Prototypical Optimal Transport for Weakly Supervised Semantic Segmentation
authors: Jian Wang, Tianhong Dai, Bingfeng Zhang, Siyue Yu, Eng Gee Lim, Jimin Xiao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15055-15064
affiliations: ["XJTLU", "University of Liverpool", "Kashmir Intelligence", "China University of Petroleum"]
cities: ["Suzhou", "Liverpool", "Srinagar", "Beijing"]
countries: ["China", "UK", "India", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_POT_Prototypical_Optimal_Transport_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2025_paper.html

abstract:
Weakly Supervised Semantic Segmentation (WSSS) leverages Class Activation Maps (CAMs) to extract spatial information from image-level labels. However, CAMs primarily highlight the most discriminative foreground regions, leading to incomplete results. Prototype-based methods attempt to address this limitation by employing prototype CAMs instead of classifier CAMs. Nevertheless, existing prototype-based methods typically use a single prototype for each class, which is insufficient to capture all attributes of the foreground features due to the significant intra-class variations across different images. Consequently, these methods still struggle with incomplete CAM predictions. In this paper, we propose a novel framework called Prototypical Optimal Transport (POT) for WSSS. POT enhances CAM predictions by dividing features into multiple clusters and activating them separately using multiple cluster prototypes. In this process, a similarity-aware optimal transport is employed to assign features to the most probable clusters. This similarity-aware strategy ensures the prioritization of significant cluster prototypes, thereby improving the accuracy of feature assignment. Additionally, we introduce an adaptive OT-based consistency loss to refine feature representations. This framework effectively overcomes the limitations of single-prototype methods, providing more complete and accurate CAM predictions. Extensive experimental results on standard WSSS benchmarks (PASCAL VOC and MS COCO) demonstrate that our method significantly improves the quality of CAMs and achieves state-of-the-art performances. The source code will be released here https://github.com/jianwang91/POT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_POT_Prototypical_Optimal_Transport_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_POT_Prototypical_Optimal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Jian and Dai, Tianhong and Zhang, Bingfeng and Yu, Siyue and Lim, Eng Gee and Xiao, Jimin},
    title     = {POT: Prototypical Optimal Transport for Weakly Supervised Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15055-15064}
}

raw_affiliation:
[{"affiliation": "XJTLU", "city": "Suzhou", "country": "China"}, {"affiliation": "University of Liverpool", "city": "Liverpool", "country": "UK"}, {"affiliation": "Kashmir Intelligence", "city": "Srinagar", "country": "India"}, {"affiliation": "China University of Petroleum", "city": "Beijing", "country": "China"}]


title: CrossOver: 3D Scene Cross-Modal Alignment
authors: Sayan Deb Sarkar, Ondrej Miksik, Marc Pollefeys, Daniel Barath, Iro Armeni
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8985-8994
affiliations: ["Stanford University", "Microsoft Spatial AI Lab", "ETH Zurich", "HUN-REN SZTAKI"]
cities: ["Stanford", "Redmond", "Zurich", "Budapest"]
countries: ["USA", "USA", "Switzerland", "Hungary"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sarkar_CrossOver_3D_Scene_Cross-Modal_Alignment_CVPR_2025_paper.html

abstract:
Multi-modal 3D object understanding has gained significant attention, yet current approaches often assume complete data availability and rigid alignment across all modalities. We present CrossOver, a novel framework for cross-modal 3D scene understanding via flexible, scene-level modality alignment. Unlike traditional methods that require aligned modality data for every object instance, CrossOver learns a unified, modality-agnostic embedding space for scenes by aligning modalities -- RGB images, point clouds, CAD models, floorplans, and text descriptions -- with relaxed constraints and without explicit object semantics. Leveraging dimensionality-specific encoders, a multi-stage training pipeline, and emergent cross-modal behaviors, CrossOver supports robust scene retrieval and object localization, even with missing modalities. Evaluations on ScanNet and 3RScan datasets show its superior performance across diverse metrics, highlighting CrossOver's adaptability for real-world applications in 3D scene understanding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sarkar_CrossOver_3D_Scene_Cross-Modal_Alignment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sarkar_CrossOver_3D_Scene_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Sarkar_2025_CVPR,
    author    = {Sarkar, Sayan Deb and Miksik, Ondrej and Pollefeys, Marc and Barath, Daniel and Armeni, Iro},
    title     = {CrossOver: 3D Scene Cross-Modal Alignment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8985-8994}
}

raw_affiliation:
[{"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "Microsoft Spatial AI Lab", "city": "Redmond", "country": "USA"}, {"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "HUN-REN SZTAKI", "city": "Budapest", "country": "Hungary"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1505-1515
affiliations: ["SKL-IOTSC, CIS, University of Macau", "Wuhan University", "COW AROBOT Co. Ltd."]
cities: ["Macau", "Wuhan", "Unknown"]
countries: ["China", "China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Rethinking_Temporal_Fusion_with_a_Unified_Gradient_Descent_View_for_CVPR_2025_paper.html

title:
Rethinking Temporal Fusion with a Unified Gradient Descent View for 3D Semantic Occupancy Prediction

authors:
Dubing Chen, Huan Zheng, Jin Fang, Xingping Dong, Xianfei Li, Wenlong Liao, Tao He, Pai Peng, Jianbing Shen

abstract:
We present GDFusion, a temporal fusion method for vision-based 3D semantic occupancy prediction (VisionOcc). GDFusion opens up the underexplored aspects of temporal fusion within the VisionOcc framework, with a focus on both temporal cues and fusion strategies. It systematically examines the entire VisionOcc pipeline, identifying three fundamental yet previously overlooked temporal cues: scene-level consistencies, motion calibration, and geometric complementation. These cues capture diverse facets of temporal evolution and provide distinctive contributions across various modules in the general VisionOcc framework. To effectively fuse temporal signals across heterogeneous representations, we propose a novel fusion strategy by reinterpreting the formulation of vanilla RNNs. This reinterpretation leverages gradient descent on features to unify the integration of diverse temporal information, seamlessly embedding the proposed temporal cues into the network. Extensive experiments on nuScenes demonstrate that GDFusion significantly outperforms established baselines, achieving 2.2%--4.7% mIoU improvement while reducing memory consumption by 30%--72%. Codes are available at https://github.com/cdb342/GDFusion.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Rethinking_Temporal_Fusion_with_a_Unified_Gradient_Descent_View_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Rethinking_Temporal_Fusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Dubing and Zheng, Huan and Fang, Jin and Dong, Xingping and Li, Xianfei and Liao, Wenlong and He, Tao and Peng, Pai and Shen, Jianbing},
    title     = {Rethinking Temporal Fusion with a Unified Gradient Descent View for 3D Semantic Occupancy Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1505-1515}
}

raw_affiliation:
[{"affiliation": "SKL-IOTSC, CIS, University of Macau", "city": "Macau", "country": "China"}, {"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "COW AROBOT Co. Ltd.", "city": "Unknown", "country": "Unknown"}]


title: SKE-Layout: Spatial Knowledge Enhanced Layout Generation with LLMs
authors: Junsheng Wang, Nieqing Cao, Yan Ding, Mengying Xie, Fuqiang Gu, Chao Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19414-19423
affiliations: ["Chongqing University", "Xi\u2019an Jiaotong-Liverpool University", "Shanghai AI Lab"]
cities: ["Chongqing", "Suzhou", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_SKE-Layout_Spatial_Knowledge_Enhanced_Layout_Generation_with_LLMs_CVPR_2025_paper.html

abstract:
Generating layouts from textual descriptions by large language models (LLMs) plays a crucial role in precise spatial reasoning-induced domains such as robotic object rearrangement and text-to-image generation. However, current methods face challenges in limited real-world examples, handling diverse layout descriptions and varying levels of granularity. To address these issues, a novel framework named Spatial Knowledge Enhanced Layout (SKE-Layout), is introduced. SKE-Layout integrates mixed spatial knowledge sources, leveraging both real and synthetic data to enhance spatial contexts. It utilizes diverse representations tailored to specific tasks and employs contrastive learning and multitask learning techniques for accurate spatial knowledge retrieval. This framework generates more accurate and fine-grained visual layouts for object rearrangement and text-to-image generation tasks, achieving improvements of 5%-30% compared to existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_SKE-Layout_Spatial_Knowledge_Enhanced_Layout_Generation_with_LLMs_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_SKE-Layout_Spatial_Knowledge_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Junsheng and Cao, Nieqing and Ding, Yan and Xie, Mengying and Gu, Fuqiang and Chen, Chao},
    title     = {SKE-Layout: Spatial Knowledge Enhanced Layout Generation with LLMs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19414-19423}
}

raw_affiliation:
[{"affiliation": "Chongqing University", "city": "Chongqing", "country": "China"}, {"affiliation": "Xi\u2019an Jiaotong-Liverpool University", "city": "Suzhou", "country": "China"}, {"affiliation": "Shanghai AI Lab", "city": "Shanghai", "country": "China"}]


title: Gaussian Eigen Models for Human Heads
authors: Wojciech Zielonka, Timo Bolkart, Thabo Beeler, Justus Thies
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15930-15940
affiliations: ["Max Planck Institute for Intelligent Systems", "Technical University of Darmstadt", "Google"]
cities: ["T\u00fcbingen", "Darmstadt", "Mountain View"]
countries: ["Germany", "Germany", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zielonka_Gaussian_Eigen_Models_for_Human_Heads_CVPR_2025_paper.html

abstract:
Current personalized neural head avatars face a trade-off: lightweight models lack detail and realism, while high-quality, animatable avatars require significant computational resources, making them unsuitable for commodity devices. To address this gap, we introduce Gaussian Eigen Models (GEM), which provide high-quality, lightweight, and easily controllable head avatars. GEM utilizes 3D Gaussian primitives for representing the appearance combined with Gaussian splatting for rendering. Building on the success of mesh-based 3D morphable face models (3DMM), we define GEM as an ensemble of linear eigenbases for representing the head appearance of a specific subject. In particular, we construct linear bases to represent the position, scale, rotation, and opacity of the 3D Gaussians. This allows us to efficiently generate Gaussian primitives of a specific head shape by a linear combination of the basis vectors, only requiring a low-dimensional parameter vector that contains the respective coefficients. We propose to construct these linear bases (GEM) by distilling high-quality compute-intense CNN-based Gaussian avatar models that can generate expression-dependent appearance changes like wrinkles. These high-quality models are trained on multi-view videos of a subject and are distilled using a series of principle component analyses. Once we have obtained the bases that represent the animatable appearance space of a specific human, we learn a regressor that takes a single RGB image as input and predicts the low-dimensional parameter vector that corresponds to the shown facial expression. We demonstrate that this regressor can be trained such that it effectively supports self- and cross-person reenactment from monocular videos without requiring prior mesh-based tracking. In a series of experiments, we compare GEM's self-reenactment and cross-person reenactment results to state-of-the-art 3D avatar methods, demonstrating GEM's higher visual quality and better generalization to new expressions. As our distilled linear model is highly efficient in generating novel animation states, we also show a real-time demo of GEMs driven by monocular webcam videos.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zielonka_Gaussian_Eigen_Models_for_Human_Heads_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zielonka_Gaussian_Eigen_Models_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zielonka_2025_CVPR,
    author    = {Zielonka, Wojciech and Bolkart, Timo and Beeler, Thabo and Thies, Justus},
    title     = {Gaussian Eigen Models for Human Heads},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15930-15940}
}

raw_affiliation:
[{"affiliation": "Max Planck Institute for Intelligent Systems", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "Technical University of Darmstadt", "city": "Darmstadt", "country": "Germany"}, {"affiliation": "Google", "city": "Mountain View", "country": "USA"}]


title: Scalable Video-to-Dataset Generation for Cross-Platform Mobile Agents
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8604-8614
affiliations: ["University of Michigan", "LG AI Research"]
cities: ["Ann Arbor", "Seoul"]
countries: ["USA", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jang_Scalable_Video-to-Dataset_Generation_for_Cross-Platform_Mobile_Agents_CVPR_2025_paper.html

authors:
Yunseok Jang, Yeda Song, Sungryull Sohn, Lajanugen Logeswaran, Tiange Luo, Dong-Ki Kim, Kyunghoon Bae, Honglak Lee

abstract:
Recent advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) have sparked significant interest in developing GUI visual agents. We introduce MONDAY (Mobile OS Navigation Task Dataset for Agents from YouTube), a large-scale dataset of 313K annotated frames from 20K instructional videos capturing diverse real-world mobile OS navigation across multiple platforms. Models that include MONDAY in their pretraining phases demonstrate robust cross-platform generalization capabilities, consistently outperforming models trained on existing single OS datasets while achieving an average performance gain of 18.11%p on an unseen mobile OS platform. To enable continuous dataset expansion as mobile platforms evolve, we present an automated framework that leverages publicly available video content to create comprehensive task datasets without manual annotation. Our framework comprises robust OCR-based scene detection (95.04% F1-score), near-perfect UI element detection (99.87% hit ratio), and novel multi-step action identification to extract reliable action sequences across diverse interface configurations. We contribute both the MONDAY dataset and our automated collection framework to facilitate future research in mobile OS navigation, available at https://github.com/runamu/monday.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jang_Scalable_Video-to-Dataset_Generation_for_Cross-Platform_Mobile_Agents_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jang_Scalable_Video-to-Dataset_Generation_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Jang_2025_CVPR,
    author    = {Jang, Yunseok and Song, Yeda and Sohn, Sungryull and Logeswaran, Lajanugen and Luo, Tiange and Kim, Dong-Ki and Bae, Kyunghoon and Lee, Honglak},
    title     = {Scalable Video-to-Dataset Generation for Cross-Platform Mobile Agents},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8604-8614}
}

raw_affiliation:
[{"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "LG AI Research", "city": "Seoul", "country": "South Korea"}]


title: Pattern Analogies: Learning to Perform Programmatic Image Edits by Analogy
authors: Aditya Ganeshan, Thibault Groueix, Paul Guerrero, Radomir Mech, Matthew Fisher, Daniel Ritchie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28715-28725
affiliations: ["Brown University", "Adobe Research"]
cities: ["Providence", "San Jose"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ganeshan_Pattern_Analogies_Learning_to_Perform_Programmatic_Image_Edits_by_Analogy_CVPR_2025_paper.html

abstract:
Pattern images are everywhere in the digital and physical worlds, and tools to edit them are valuable. But editing pattern images is tricky: desired edits are often *programmatic*: structure-aware edits that alter the underlying program which generates the pattern. One could attempt to infer this underlying program, but current methods for doing so struggle with complex images and produce unorganized programs that make editing tedious. In this work, we introduce a novel approach to perform programmatic edits on pattern images. By using a *pattern analogy*&mdash;a pair of simple patterns to demonstrate the intended edit&mdash;and a learning-based generative model to execute these edits, our method allows users to intuitively edit patterns. To enable this paradigm, we introduce **SplitWeave**, a domain-specific language that, combined with a framework for sampling synthetic pattern analogies, enables the creation of a large, high-quality synthetic training dataset. We also present **TriFuser**, a Latent Diffusion Model (LDM) designed to overcome critical issues that arise when naively deploying LDMs to this task. Extensive experiments on real-world, artist-sourced patterns reveals that our method faithfully performs the demonstrated edit while also generalizing to related pattern styles beyond its training distribution.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ganeshan_Pattern_Analogies_Learning_to_Perform_Programmatic_Image_Edits_by_Analogy_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ganeshan_Pattern_Analogies_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ganeshan_2025_CVPR,
    author    = {Ganeshan, Aditya and Groueix, Thibault and Guerrero, Paul and Mech, Radomir and Fisher, Matthew and Ritchie, Daniel},
    title     = {Pattern Analogies: Learning to Perform Programmatic Image Edits by Analogy},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28715-28725}
}

raw_affiliation:
[{"affiliation": "Brown University", "city": "Providence", "country": "USA"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}]


title: 4D-Fly: Fast 4D Reconstruction from a Single Monocular Video
authors: Diankun Wu, Fangfu Liu, Yi-Hsin Hung, Yue Qian, Xiaohang Zhan, Yueqi Duan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16663-16673
affiliations: ["Tsinghua University", "Tencent"]
cities: ["Beijing", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_4D-Fly_Fast_4D_Reconstruction_from_a_Single_Monocular_Video_CVPR_2025_paper.html

abstract:
4D reconstruction from a single monocular video is an important but challenging task due to its inherent under-constrained nature. While most existing 4D reconstruction methods focus on multi-camera settings, they always suffer from limited multi-view information in monocular videos. Recent studies have attempted to mitigate the ill-posed problem by incorporating data-driven priors as additional supervision. However, they require hours of optimization to align the splatted 2D feature maps of explicit Gaussians with various priors, which limits the range of applications. To address the time-consuming issue, we propose 4D-Fly, an efficient and effective framework for reconstructing the 4D scene from a monocular video (hundreds of frames within 6 minutes), more than 20 xfaster and even achieving higher quality than previous optimization methods. Our key insight is to unleash the explicit property of Gaussian primitives and directly apply data priors to them. Specifically, we build a streaming 4D reconstruction paradigm that includes: propagating existing Gaussian to the next timestep with an anchor-based strategy, expanding the 4D scene map with the canonical Gaussian map, and an efficient 4D scene optimization process to further improve visual quality and motion accuracy. Extensive experiments demonstrate the superiority of our 4D-Fly over state-of-the-art methods in terms of speed and quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_4D-Fly_Fast_4D_Reconstruction_from_a_Single_Monocular_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_4D-Fly_Fast_4D_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Diankun and Liu, Fangfu and Hung, Yi-Hsin and Qian, Yue and Zhan, Xiaohang and Duan, Yueqi},
    title     = {4D-Fly: Fast 4D Reconstruction from a Single Monocular Video},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16663-16673}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Tencent", "city": "Shenzhen", "country": "China"}]


authors: Zikuan Li, Honghua Chen, Yuecheng Wang, Sibo Wu, Mingqiang Wei, Jun Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27254-27263
affiliations: ["Nanjing University of Aeronautics and Astronautics"]
cities: ["Nanjing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_STAR-Edge_Structure-aware_Local_Spherical_Curve_Representation_for_Thin-walled_Edge_Extraction_CVPR_2025_paper.html

title:
STAR-Edge: Structure-aware Local Spherical Curve Representation for Thin-walled Edge Extraction from Unstructured Point Clouds

abstract:
Extracting geometric edges from unstructured point clouds remains a significant challenge, particularly in thin-walled structures that are commonly found in everyday objects. Traditional geometric methods and recent learning-based approaches frequently struggle with these structures, as both rely heavily on sufficient contextual information from local point neighborhoods. However, 3D measurement data of thin-walled structures often lack the accurate, dense, and regular neighborhood sampling required for reliable edge extraction, resulting in degraded performance. In this work, we introduce STAR-Edge, a novel approach designed for detecting and refining edge points in thin-walled structures. Our method leverages a unique representation, the local spherical curve, to create structure-aware neighborhoods that emphasize co-planar points while reducing interference from close-by, non-co-planar surfaces. This representation is transformed into a rotation-invariant descriptor, which, combined with a lightweight multi-layer perceptron, enables robust edge point classification even in the presence of noise and sparse or irregular sampling. Besides, we also use the local spherical curve representation to estimate more precise normals and introduce an optimization function to project initially identified edge points exactly on the true edges. Experiments conducted on the ABC dataset and thin-walled structure-specific datasets demonstrate that STAR-Edge outperforms existing edge detection methods, showcasing better robustness under various challenging conditions. The source code is available at https://github.com/Miraclelzk/STAR-Edge.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_STAR-Edge_Structure-aware_Local_Spherical_Curve_Representation_for_Thin-walled_Edge_Extraction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_STAR-Edge_Structure-aware_Local_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Zikuan and Chen, Honghua and Wang, Yuecheng and Wu, Sibo and Wei, Mingqiang and Wang, Jun},
    title     = {STAR-Edge: Structure-aware Local Spherical Curve Representation for Thin-walled Edge Extraction from Unstructured Point Clouds},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27254-27263}
}

raw_affiliation:
[{"affiliation": "Nanjing University of Aeronautics and Astronautics", "city": "Nanjing", "country": "China"}]


title: Tokenize Image Patches: Global Context Fusion for Effective Haze Removal in Large Images
authors: Jiuchen Chen, Xinyu Yan, Qizhi Xu, Kaiqi Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2258-2268
raw_affiliation: [{"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}]
affiliations: ["Beijing Institute of Technology"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Tokenize_Image_Patches_Global_Context_Fusion_for_Effective_Haze_Removal_CVPR_2025_paper.html

abstract:
Global contextual information and local detail features are essential for haze removal tasks. Deep learning models perform well on small, low-resolution images, but they encounter difficulties with large, high-resolution ones due to GPU memory limitations. As a compromise, they often resort to image slicing or downsampling. The former diminishes global information, while the latter discards high-frequency details. To address these challenges, we propose DehazeXL, a haze removal method that effectively balances global context and local feature extraction, enabling end-to-end modeling of large images on mainstream GPU hardware. Additionally, to evaluate the efficiency of global context utilization in haze removal performance, we design a visual attribution method tailored to the characteristics of haze removal tasks. Finally, recognizing the lack of benchmark datasets for haze removal in large images, we have developed an ultra-high-resolution haze removal dataset (8KDehaze) to support model training and testing. It includes 10000 pairs of clear and hazy remote sensing images, each sized at 8192 x8192 pixels. Extensive experiments demonstrate that DehazeXL can infer images up to 10240 x10240 pixels with only 21 GB of memory, achieving state-of-the-art results among all evaluated methods. The source code and experimental dataset will soon be made publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Tokenize_Image_Patches_Global_Context_Fusion_for_Effective_Haze_Removal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Tokenize_Image_Patches_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Jiuchen and Yan, Xinyu and Xu, Qizhi and Li, Kaiqi},
    title     = {Tokenize Image Patches: Global Context Fusion for Effective Haze Removal in Large Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2258-2268}
}


authors: Yuchen Wang, Hongyuan Wang, Lizhi Wang, Xin Wang, Lin Zhu, Wanxuan Lu, Hua Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12679-12689
cities: ["Beijing", "Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Complementary_Advantages_Exploiting_Cross-Field_Frequency_Correlation_for_NIR-Assisted_Image_Denoising_CVPR_2025_paper.html

title:
Complementary Advantages: Exploiting Cross-Field Frequency Correlation for NIR-Assisted Image Denoising

abstract:
Existing single-image denoising algorithms often struggle to restore details when dealing with complex noisy images. The introduction of near-infrared (NIR) images offers new possibilities for RGB image denoising. However, due to the inconsistency between NIR and RGB images, the existing works still struggle to balance the contributions of two fields in the process of image fusion. In response to this, in this paper, we develop a cross-field Frequency Correlation Exploiting Network (FCENet) for NIR-assisted image denoising. We first propose the frequency correlation prior based on an in-depth statistical frequency analysis of NIR-RGB image pairs. The prior reveals the complementary correlation of NIR and RGB images in the frequency domain. Leveraging frequency correlation prior, we then establish a frequency learning framework composed of Frequency Dynamic Selection Mechanism (FDSM) and Frequency Exhaustive Fusion Mechanism (FEFM). FDSM dynamically selects complementary information from NIR and RGB images in the frequency domain, and FEFM strengthens the control of common and differential features during the fusion process of NIR and RGB features. Extensive experiments on simulated and real data validate that the proposed method outperforms other state-of-the-art methods. The code will be released at https://github.com/yuchenwang815/FCENet.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Complementary_Advantages_Exploiting_Cross-Field_Frequency_Correlation_for_NIR-Assisted_Image_Denoising_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Complementary_Advantages_Exploiting_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yuchen and Wang, Hongyuan and Wang, Lizhi and Wang, Xin and Zhu, Lin and Lu, Wanxuan and Huang, Hua},
    title     = {Complementary Advantages: Exploiting Cross-Field Frequency Correlation for NIR-Assisted Image Denoising},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12679-12689}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, Beijing Institute of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Beijing Normal University", "city": "Beijing", "country": "China"}, {"affiliation": "Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education", "city": "Beijing", "country": "China"}, {"affiliation": "Aerospace Information Research Institute, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["School of Computer Science and Technology, Beijing Institute of Technology", "School of Artificial Intelligence, Beijing Normal University", "Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education", "Aerospace Information Research Institute, Chinese Academy of Sciences"]


title: Eval3D: Interpretable and Fine-grained Evaluation for 3D Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13326-13336
cities: ["Cambridge", "Seattle", "New York", "London", "Seattle", "Ithaca"]
countries: ["USA", "USA", "USA", "UK", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Duggal_Eval3D_Interpretable_and_Fine-grained_Evaluation_for_3D_Generation_CVPR_2025_paper.html

authors:
Shivam Duggal, Yushi Hu, Oscar Michel, Aniruddha Kembhavi, William T. Freeman, Noah A. Smith, Ranjay Krishna, Antonio Torralba, Ali Farhadi, Wei-Chiu Ma

abstract:
Despite the unprecedented progress in the field of 3D generation, current systems still often fail to produce high-quality 3D assets that are visually appealing and geometrically and semantically consistent across multiple viewpoints. To effectively assess the quality of the generated 3D data, there is a need for a reliable 3D evaluation tool. Unfortunately, existing 3D evaluation metrics often overlook the geometric quality of generated assets or merely rely on black-box multimodal large language models for coarse assessment. In this paper, we introduce Eval3D, a fine-grained, interpretable evaluation tool that can faithfully evaluate the quality of generated 3D assets based on various distinct yet complementary criteria. Our key observation is that many desired properties of 3D generation, such as semantic and geometric consistency, can be effectively captured by measuring the consistency among various foundation models and tools. We thus leverage a diverse set of models and tools as probes to evaluate the inconsistency of generated 3D assets across different aspects. Compared to prior work, Eval3D provides pixel-wise measurement, enables accurate 3D spatial feedback, and aligns more closely with human judgments. We comprehensively evaluate existing 3D generation models using Eval3D and highlight the limitations and challenges of current models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Duggal_Eval3D_Interpretable_and_Fine-grained_Evaluation_for_3D_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Duggal_Eval3D_Interpretable_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Duggal_2025_CVPR,
    author    = {Duggal, Shivam and Hu, Yushi and Michel, Oscar and Kembhavi, Aniruddha and Freeman, William T. and Smith, Noah A. and Krishna, Ranjay and Torralba, Antonio and Farhadi, Ali and Ma, Wei-Chiu},
    title     = {Eval3D: Interpretable and Fine-grained Evaluation for 3D Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13326-13336}
}

raw_affiliation:
[{"affiliation": "Massachusetts Institute of Technology", "city": "Cambridge", "country": "USA"}, {"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "New York University", "city": "New York", "country": "USA"}, {"affiliation": "Wayve", "city": "London", "country": "UK"}, {"affiliation": "Allen Institute for AI", "city": "Seattle", "country": "USA"}, {"affiliation": "Cornell University", "city": "Ithaca", "country": "USA"}]

affiliations:
["Massachusetts Institute of Technology", "University of Washington", "New York University", "Wayve", "Allen Institute for AI", "Cornell University"]


authors: Rong Qin, Xingyu Liu, Jinglei Shi, Liang Lin, Jufeng Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25960-25970
cities: ["Tianjin", "Shenzhen", "Shenzhen", "Guangzhou", "Dalian"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qin_Boosting_the_Dual-Stream_Architecture_in_Ultra-High_Resolution_Segmentation_with_Resolution-Biased_CVPR_2025_paper.html

title:
Boosting the Dual-Stream Architecture in Ultra-High Resolution Segmentation with Resolution-Biased Uncertainty Estimation

abstract:
Over the last decade, significant efforts have been dedicated to designing efficient models for the challenge of ultra-high resolution (UHR) semantic segmentation. These models mainly follow the dual-stream architecture and generally fall into three subcategories according to the improvement objectives, i.e., dual-stream ensemble, selective zoom, and complementary learning. However, most of them overly concentrate on crafting complex pipelines to pursue one of the above objectives separately, limiting the model performance in both accuracy and inference consumption. In this paper, we suggest simultaneously achieving these objectives by estimating resolution-biased uncertainties in low resolution stream. Here, the resolution-biased uncertainty refers to the degree of prediction unreliability primarily caused by resolution loss from down-sampling operations. Specifically, we propose a dual-stream UHR segmentation framework, where an estimator is used to assess resolution-biased uncertainties through the entropy map and high-frequency feature residual. The framework also includes a selector, an ensembler, and a complementer to boost the model with obtained estimations. They share the uncertainty estimations as the weights to choose difficult regions as the inputs for UHR stream, perform weighted fusion between distinct streams, and enhance the learning for important pixels, respectively. Experiment results demonstrate that our method achieves a satisfactory balance between accuracy and inference consumption against other state-of-the-art (SOTA) methods. The code is available in the https://github.com/Qinrong-NKU/RUE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qin_Boosting_the_Dual-Stream_Architecture_in_Ultra-High_Resolution_Segmentation_with_Resolution-Biased_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Qin_2025_CVPR,
    author    = {Qin, Rong and Liu, Xingyu and Shi, Jinglei and Lin, Liang and Yang, Jufeng},
    title     = {Boosting the Dual-Stream Architecture in Ultra-High Resolution Segmentation with Resolution-Biased Uncertainty Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25960-25970}
}

raw_affiliation:
[{"affiliation": "VCIP & TMCC & DISSec, College of Computer Science, Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Nankai International Advanced Research Institute (SHENZHEN\u00b7FUTIAN)", "city": "Shenzhen", "country": "China"}, {"affiliation": "School of Computer Science and Engineering, Sun Y at-Sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Key Lab of SCCI, Dalian University of Technology", "city": "Dalian", "country": "China"}]

affiliations:
["VCIP & TMCC & DISSec, College of Computer Science, Nankai University", "Pengcheng Laboratory", "Nankai International Advanced Research Institute (SHENZHEN\u00b7FUTIAN)", "School of Computer Science and Engineering, Sun Y at-Sen University", "Key Lab of SCCI, Dalian University of Technology"]


title: DiffLO: Semantic-Aware LiDAR Odometry with Diffusion-Based Refinement
authors: Yongshu Huang, Chen Liu, Minghang Zhu, Sheng Ao, Chenglu Wen, Cheng Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17050-17059
cities: ["Xiamen", "Xiamen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_DiffLO_Semantic-Aware_LiDAR_Odometry_with_Diffusion-Based_Refinement_CVPR_2025_paper.html

abstract:
LiDAR odometry is a critical module in autonomous driving systems, responsible for accurate localization by estimating the relative pose transformation between consecutive point cloud frames. However, existing studies frequently encounter challenges with unreliable pose estimation, due to the lack of in-depth understanding of scenario and the presence of noise interference. To address this challenge, we propose DiffLO, a semantic-aware LiDAR odometry network with diffusion-based refinement. To mitigate the impact of challenging cases such as dynamic, repetitive patterns, and low textures, we introduce a semantic distillation method that integrates semantic information into the odometry task. This allows the network to gain a semantic understanding of the scene, enabling it to focus more on the objects that are beneficial for pose estimation. Additionally, to enhance the robustness, we propose a diffusion-based refinement method. This method uses pose-related features as conditional constraints for generative diversity, iteratively refining the pose estimation to achieve greater accuracy. Comparative experiments on the KITTI odometry dataset demonstrate that the proposed method achieves state-of-the-art performance among existing learning-based approaches. Furthermore, the proposed DiffLO method outperforms the classic A-LOAM on most evaluation sequences.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_DiffLO_Semantic-Aware_LiDAR_Odometry_with_Diffusion-Based_Refinement_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_DiffLO_Semantic-Aware_LiDAR_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Yongshu and Liu, Chen and Zhu, Minghang and Ao, Sheng and Wen, Chenglu and Wang, Cheng},
    title     = {DiffLO: Semantic-Aware LiDAR Odometry with Diffusion-Based Refinement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17050-17059}
}

raw_affiliation:
[{"affiliation": "Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, China.", "city": "Xiamen", "country": "China"}, {"affiliation": "Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China.", "city": "Xiamen", "country": "China"}]

affiliations:
["Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, China.", "Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China."]


title: pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation
authors: Yifei Zhang, Hao Zhu, Alysa Ziying Tan, Dianzhi Yu, Longtao Huang, Han Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30640-30650
cities: ["Singapore", "Sydney", "Hong Kong", "Hangzhou"]
countries: ["Singapore", "Australia", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_pFedMxF_Personalized_Federated_Class-Incremental_Learning_with_Mixture_of_Frequency_Aggregation_CVPR_2025_paper.html

abstract:
Federated learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative machine learning. However, extending FL to class incremental learning settings introduces three key challenges: 1) spatial heterogeneity due to non-IID data distributions across clients, 2) temporal heterogeneity due to sequential arrival of tasks, and 3) resource heterogeneity due to diverse client capabilities. Existing approaches generally address these challenges in isolation, potentially leading to interference between updates, catastrophic forgetting, or excessive communication overhead. In this paper, we propose personalized Federated class-incremental parameter efficient fine-tuning with Mixture of Frequency aggregation (pFedMixF), a novel framework that simultaneously addresses all three heterogeneity challenges through frequency domain decomposition. Our key insight is that assigning orthogonal frequency components to different clients and tasks enables interference-free learning to be achieved with minimal communication costs. We further design an Auto-Task Agnostic Classifier that automatically routes samples to task-specific classifiers while adapting to heterogeneous class distributions.We conduct extensive experiments on three benchmark datasets, comparing our approach with eight state-of-the-art methods. The results demonstrate that \methodname   achieves comparable test accuracy while requiring only 25% of the entire model parameters and incurring significantly lower communication costs than baseline methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_pFedMxF_Personalized_Federated_Class-Incremental_Learning_with_Mixture_of_Frequency_Aggregation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_pFedMxF_Personalized_Federated_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Yifei and Zhu, Hao and Tan, Alysa Ziying and Yu, Dianzhi and Huang, Longtao and Yu, Han},
    title     = {pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30640-30650}
}

raw_affiliation:
[{"affiliation": "College of Computing and Data Science, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Data61 CSRIO", "city": "Sydney", "country": "Australia"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Alibaba Group", "city": "Hangzhou", "country": "China"}]

affiliations:
["College of Computing and Data Science, Nanyang Technological University", "Data61 CSRIO", "The Chinese University of Hong Kong", "Alibaba Group"]


title: Style-Editor: Text-driven Object-centric Style Editing
authors: Jihun Park, Jongmin Gim, Kyoungmin Lee, Seunghun Lee, Sunghoon Im
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18281-18291
raw_affiliation: [{"affiliation": "DGIST", "city": "Daegu", "country": "Republic of Korea"}]
affiliations: ["DGIST"]
cities: ["Daegu"]
countries: ["Republic of Korea"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Park_Style-Editor_Text-driven_Object-centric_Style_Editing_CVPR_2025_paper.html

abstract:
We present Text-driven object-centric style editing model named Style-Editor, a novel method that guides style editing at an object-centric level using textual inputs.The core of Style-Editor is our Patch-wise Co-Directional (PCD) loss, meticulously designed for precise object-centric editing that are closely aligned with the input text. This loss combines a patch directional loss for text-guided style direction and a patch distribution consistency loss for even CLIP embedding distribution across object regions. It ensures a seamless and harmonious style editing across object regions.Key to our method are the Text-Matched Patch Selection (TMPS) and Pre-fixed Region Selection (PRS) modules for identifying object locations via text, eliminating the need for segmentation masks. Lastly, we introduce an Adaptive Background Preservation (ABP) loss to maintain the original style and structural essence of the image's background. This loss is applied to dynamically identified background areas.Extensive experiments underline the effectiveness of our approach in creating visually coherent and textually aligned style editing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Park_Style-Editor_Text-driven_Object-centric_Style_Editing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_Style-Editor_Text-driven_Object-centric_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Park_2025_CVPR,
    author    = {Park, Jihun and Gim, Jongmin and Lee, Kyoungmin and Lee, Seunghun and Im, Sunghoon},
    title     = {Style-Editor: Text-driven Object-centric Style Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18281-18291}
}


title: Transfer Your Perspective: Controllable 3D Generation from Any Viewpoint in a Driving Scene
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12027-12036
affiliations: ["The Ohio State University", "Cornell University"]
cities: ["Columbus", "Ithaca"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pan_Transfer_Your_Perspective_Controllable_3D_Generation_from_Any_Viewpoint_in_CVPR_2025_paper.html

authors:
Tai-Yu Pan, Sooyoung Jeon, Mengdi Fan, Jinsu Yoo, Zhenyang Feng, Mark Campbell, Kilian Q. Weinberger, Bharath Hariharan, Wei-Lun Chao

abstract:
Self-driving cars relying solely on ego-centric perception face limitations in sensing, often failing to detect occluded, faraway objects. Collaborative autonomous driving (CAV) seems like a promising direction, but collecting data for development is non-trivial. It requires placing multiple sensor-equipped agents in a real-world driving scene, simultaneously! As such, existing datasets are limited in locations and agents. We introduce a novel surrogate to the rescue, which is to generate realistic perception from different viewpoints in a driving scene, conditioned on a real-world sample -- the ego-car's sensory data. This surrogate has huge potential: it could potentially turn any ego-car dataset into a collaborative driving one to scale up the development of CAV. We present the very first solution, using a combination of synthetic collaborative data and real ego-car data. Our method, Transfer Your Perspective (TYP), learns a conditioned diffusion model whose output samples are not only realistic but also consistent in both semantics and layouts with the given ego-car data. Empirical results demonstrate TYP's effectiveness in aiding in a CAV setting. In particular, TYP enables us to (pre-)train collaborative perception algorithms like early and late fusion with little or no real-world collaborative data, greatly facilitating downstream CAV applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pan_Transfer_Your_Perspective_Controllable_3D_Generation_from_Any_Viewpoint_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pan_Transfer_Your_Perspective_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pan_2025_CVPR,
    author    = {Pan, Tai-Yu and Jeon, Sooyoung and Fan, Mengdi and Yoo, Jinsu and Feng, Zhenyang and Campbell, Mark and Weinberger, Kilian Q. and Hariharan, Bharath and Chao, Wei-Lun},
    title     = {Transfer Your Perspective: Controllable 3D Generation from Any Viewpoint in a Driving Scene},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12027-12036}
}

raw_affiliation:
[{"affiliation": "The Ohio State University", "city": "Columbus", "country": "USA"}, {"affiliation": "Cornell University", "city": "Ithaca", "country": "USA"}]


title: Efficient Transfer Learning for Video-language Foundation Models
authors: Haoxing Chen, Zizheng Huang, Yan Hong, Yanshuo Wang, Zhongcai Lyu, Zhuoer Xu, Jun Lan, Zhangxuan Gu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29129-29138
cities: ["Hangzhou", "Nanjing", "Shanghai", "Canberra"]
countries: ["China", "China", "China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Efficient_Transfer_Learning_for_Video-language_Foundation_Models_CVPR_2025_paper.html

abstract:
Pre-trained vision-language models provide a robust foundation for efficient transfer learning across various downstream tasks. In the field of video action recognition, mainstream approaches often introduce additional modules to capture temporal information. Although the additional modules increase the capacity of model, enabling it to better capture video-specific inductive biases, existing methods typically introduce a substantial number of new parameters and are prone to catastrophic forgetting of previously acquired generalizable knowledge. In this paper, we propose a parameter-efficient Multi-modal Spatio-Temporal Adapter (MSTA) to enhance the alignment between textual and visual representations, achieving a balance between generalizable knowledge and task-specific adaptation. Furthermore, to mitigate over-fitting and enhance generalizability, we introduce a spatio-temporal description-guided consistency constraint. This constraint involves providing template inputs (e.g., "a video of \ cls\ ") to the trainable language branch and LLM-generated spatio-temporal descriptions to the pre-trained language branch, enforcing output consistency between the branches. This approach reduces overfitting to downstream tasks and enhances the distinguishability of the trainable branch within the spatio-temporal semantic space. We evaluate the effectiveness of our approach across four tasks: zero-shot transfer, few-shot learning, base-to-novel generalization, and fully-supervised learning. Compared to many state-of-the-art methods, our MSTA achieves outstanding performance across all evaluations, while using only 2-7% of the trainable parameters in the original model.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Efficient_Transfer_Learning_for_Video-language_Foundation_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Efficient_Transfer_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Haoxing and Huang, Zizheng and Hong, Yan and Wang, Yanshuo and Lyu, Zhongcai and Xu, Zhuoer and Lan, Jun and Gu, Zhangxuan},
    title     = {Efficient Transfer Learning for Video-language Foundation Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29129-29138}
}

raw_affiliation:
[{"affiliation": "Tiansuan Lab, Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Nanjing Univeristy", "city": "Nanjing", "country": "China"}, {"affiliation": "Shanghai Innovation Institute", "city": "Shanghai", "country": "China"}, {"affiliation": "Australian National University", "city": "Canberra", "country": "Australia"}]

affiliations:
["Tiansuan Lab, Ant Group", "Nanjing Univeristy", "Shanghai Innovation Institute", "Australian National University"]


title: Radio Frequency Ray Tracing with Neural Object Representation for Enhanced RF Modeling
authors: Xingyu Chen, Zihao Feng, Kun Qian, Xinyu Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21339-21348
affiliations: ["UC San Diego", "UC San Diego", "University of Virginia", "UC San Diego"]
cities: ["San Diego", "San Diego", "Charlottesville", "San Diego"]
countries: ["USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Radio_Frequency_Ray_Tracing_with_Neural_Object_Representation_for_Enhanced_CVPR_2025_paper.html

abstract:
Radio frequency (RF) propagation modeling poses unique electromagnetic simulation challenges. While recent neural representations have shown success in visible spectrum rendering, the fundamentally different scales and physics of RF signals require novel modeling paradigms. In this paper, we introduce RFScape, a novel framework that bridges the gap between neural scene representation and RF propagation modeling. Our key insight is that complex RF-object interactions can be captured through object-centric neural representations while preserving the composability of traditional ray tracing. Unlike previous approaches that either rely on crude geometric approximations or require dense spatial sampling of entire scenes, RFScape learns per-object electromagnetic properties and enables flexible scene composition. Through extensive evaluation on real-world RF testbeds, we demonstrate that our approach achieves 13 dB improvement over conventional ray tracing and 5 dB over state-of-the-art neural baselines in modeling accuracy, while requiring only sparse training samples.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Radio_Frequency_Ray_Tracing_with_Neural_Object_Representation_for_Enhanced_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Xingyu and Feng, Zihao and Qian, Kun and Zhang, Xinyu},
    title     = {Radio Frequency Ray Tracing with Neural Object Representation for Enhanced RF Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21339-21348}
}

raw_affiliation:
[{"affiliation": "UC San Diego", "city": "San Diego", "country": "USA"}, {"affiliation": "UC San Diego", "city": "San Diego", "country": "USA"}, {"affiliation": "University of Virginia", "city": "Charlottesville", "country": "USA"}, {"affiliation": "UC San Diego", "city": "San Diego", "country": "USA"}]


title: ANNEXE: Unified Analyzing, Answering, and Pixel Grounding for Egocentric Interaction
authors: Yuejiao Su, Yi Wang, Qiongyang Hu, Chuang Yang, Lap-Pui Chau
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9027-9038
cities: ["Hong Kong"]
countries: ["Hong Kong SAR"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Su_ANNEXE_Unified_Analyzing_Answering_and_Pixel_Grounding_for_Egocentric_Interaction_CVPR_2025_paper.html

abstract:
Egocentric interaction perception is one of the essential branches in investigating human-environment interaction, which lays the basis for developing next-generation intelligent systems. However, existing egocentric interaction understanding methods cannot yield coherent textual and pixel-level responses simultaneously according to user queries, which lacks flexibility for varying downstream application requirements. To comprehend egocentric interactions exhaustively, this paper presents a novel task named Egocentric Interaction Reasoning and pixel Grounding (Ego-IRG). Taking an egocentric image with the query as input, Ego-IRG is the first task that aims to resolve the interactions through three crucial steps: analyzing, answering, and pixel grounding, which results in fluent textual and fine-grained pixel-level responses. Another challenge is that existing datasets cannot meet the conditions for the Ego-IRG task. To address this limitation, this paper creates the Ego-IRGBench dataset based on extensive manual efforts, which includes over 20k egocentric images with 1.6 million queries and corresponding multimodal responses about interactions. Moreover, we design a unified ANNEXE model to generate text- and pixel-level outputs utilizing multimodal large language models, which enables a comprehensive interpretation of egocentric interactions. The experiments on the Ego-IRGBench exhibit the effectiveness of our ANNEXE model compared with other works.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Su_ANNEXE_Unified_Analyzing_Answering_and_Pixel_Grounding_for_Egocentric_Interaction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Su_ANNEXE_Unified_Analyzing_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Su_2025_CVPR,
    author    = {Su, Yuejiao and Wang, Yi and Hu, Qiongyang and Yang, Chuang and Chau, Lap-Pui},
    title     = {ANNEXE: Unified Analyzing, Answering, and Pixel Grounding for Egocentric Interaction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9027-9038}
}

raw_affiliation:
[{"affiliation": "Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong SAR", "city": "Hong Kong", "country": "Hong Kong SAR"}]

affiliations:
["Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong SAR"]


title: MET3R: Measuring Multi-View Consistency in Generated Images
authors: Mohammad Asim, Christopher Wewer, Thomas Wimmer, Bernt Schiele, Jan Eric Lenssen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6034-6044
affiliations: ["Max Planck Institute for Informatics", "ETH Zurich"]
cities: ["Saarbr\u00fccken", "Z\u00fcrich"]
countries: ["Germany", "Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Asim_MET3R_Measuring_Multi-View_Consistency_in_Generated_Images_CVPR_2025_paper.html

abstract:
We introduce MEt3R, a metric for multi-view consistency in generated images. Large-scale generative models for multi-view image generation are rapidly advancing the field of 3D inference from sparse observations. However, due to the nature of generative modeling, traditional reconstruction metrics are not suitable to measure the quality of generated outputs and metrics that are independent of the sampling procedure are desperately needed. In this work, we specifically address the aspect of consistency between generated multi-view images, which can be evaluated independently of the specific scene. Our approach uses DUSt3R to obtain dense 3D reconstructions from image pairs in a feed-forward manner, which are used to warp image contents from one view into the other. Then, feature maps of these images are compared to obtain a similarity score that is invariant to view-dependent effects. Using MEt3R, we evaluate the consistency of a large set of previous methods for novel view and video generation, including our open, multi-view latent diffusion model. Code is available online: geometric-rl.mpi-inf.mpg.de/met3r/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Asim_MET3R_Measuring_Multi-View_Consistency_in_Generated_Images_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Asim_MET3R_Measuring_Multi-View_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Asim_2025_CVPR,
    author    = {Asim, Mohammad and Wewer, Christopher and Wimmer, Thomas and Schiele, Bernt and Lenssen, Jan Eric},
    title     = {MET3R: Measuring Multi-View Consistency in Generated Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6034-6044}
}

raw_affiliation:
[{"affiliation": "Max Planck Institute for Informatics", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "ETH Zurich", "city": "Z\u00fcrich", "country": "Switzerland"}]


title: Segmenting Maxillofacial Structures in CBCT Volumes
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5238-5248
cities: ["Modena", "Nijmegen", "Berlin"]
countries: ["Italy", "the Netherlands", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bolelli_Segmenting_Maxillofacial_Structures_in_CBCT_Volumes_CVPR_2025_paper.html

authors:
Federico Bolelli, Kevin Marchesini, Niels van Nistelrooij, Luca Lumetti, Vittorio Pipoli, Elisa Ficarra, Shankeeth Vinayahalingam, Costantino Grana

abstract:
Cone-beam computed tomography (CBCT) is a standard imaging modality in orofacial and dental practices, providing essential 3D volumetric imaging of anatomical structures, including jawbones, teeth, sinuses, and neurovascular canals. Accurately segmenting these structures is fundamental to numerous clinical applications, such as surgical planning and implant placement. However, manual segmentation of CBCT scans is time-intensive and requires expert input, creating a demand for automated solutions through deep learning. Effective development of such algorithms relies on access to large, well-annotated datasets, yet current datasets are often privately stored or limited in scope and considered structures, especially concerning 3D annotations. This paper proposes ToothFairy2, a comprehensive, publicly accessible CBCT dataset with voxel-level 3D annotations of 42 distinct classes corresponding to maxillofacial structures. We validate the dataset by benchmarking state-of-the-art neural network models, including convolutional, transformer-based, and hybrid Mamba-based architectures, to evaluate segmentation performance across complex anatomical regions. Our work also explores adaptations to the nnU-Net framework to optimize multi-class segmentation for maxillofacial anatomy. The proposed dataset provides a fundamental resource for advancing maxillofacial segmentation and supports future research in automated 3D image analysis in digital dentistry.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bolelli_Segmenting_Maxillofacial_Structures_in_CBCT_Volumes_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Bolelli_2025_CVPR,
    author    = {Bolelli, Federico and Marchesini, Kevin and van Nistelrooij, Niels and Lumetti, Luca and Pipoli, Vittorio and Ficarra, Elisa and Vinayahalingam, Shankeeth and Grana, Costantino},
    title     = {Segmenting Maxillofacial Structures in CBCT Volumes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5238-5248}
}

raw_affiliation:
[{"affiliation": "University of Modena and Reggio Emilia, Italy", "city": "Modena", "country": "Italy"}, {"affiliation": "Radboud University Medical Center, the Netherlands", "city": "Nijmegen", "country": "the Netherlands"}, {"affiliation": "Charit\u00b4e \u2013 Universit\u00a8atsmedizin Berlin, Germany", "city": "Berlin", "country": "Germany"}]

affiliations:
["University of Modena and Reggio Emilia, Italy", "Radboud University Medical Center, the Netherlands", "Charit\u00b4e \u2013 Universit\u00a8atsmedizin Berlin, Germany"]


title: 3D Dental Model Segmentation with Geometrical Boundary Preserving
authors: Shufan Xi, Zexian Liu, Junlin Chang, Hongyu Wu, Xiaogang Wang, Aimin Hao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10476-10485
cities: ["Beijing", "Chongqing", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xi_3D_Dental_Model_Segmentation_with_Geometrical_Boundary_Preserving_CVPR_2025_paper.html

abstract:
3D intraoral scan mesh is widely used in digital dentistry diagnosis, segmenting 3D intraoral scan mesh is a critical preliminary task. Numerous approaches have been devised for precise tooth segmentation.  Currently, the deep learning-based methods are capable of the high accuracy segmentation of crown. However, the segmentation accuracy at the junction between the crown and the gum is still below average. Existing down-sampling methods are unable to effectively preserve the geometric details at the junction. To address these problems, we propose CrossTooth, a boundary-preserving segmentation method that combines 3D mesh selective downsampling to retain more vertices at the tooth-gingiva area, along with cross-modal discriminative boundary features extracted from multi-view rendered images, enhancing the geometric representation of the segmentation network. Using a point network as a backbone and incorporating image complementary features, CrossTooth significantly improves segmentation accuracy, as demonstrated by experiments on a public intraoral scan dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xi_3D_Dental_Model_Segmentation_with_Geometrical_Boundary_Preserving_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Xi_2025_CVPR,
    author    = {Xi, Shufan and Liu, Zexian and Chang, Junlin and Wu, Hongyu and Wang, Xiaogang and Hao, Aimin},
    title     = {3D Dental Model Segmentation with Geometrical Boundary Preserving},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10476-10485}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Virtual Reality Technology and Systems, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "College of Computer and Information Science, Southwest University", "city": "Chongqing", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}]

affiliations:
["State Key Laboratory of Virtual Reality Technology and Systems, Beihang University", "College of Computer and Information Science, Southwest University", "Peng Cheng Laboratory"]


title: Neuro-3D: Towards 3D Visual Decoding from EEG Signals
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23870-23880
cities: ["Shanghai", "Beijing", "Hong Kong", "Shanghai", "Shanghai", "Guangzhou"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Neuro-3D_Towards_3D_Visual_Decoding_from_EEG_Signals_CVPR_2025_paper.html

authors:
Zhanqiang Guo, Jiamin Wu, Yonghao Song, Jiahui Bu, Weijian Mai, Qihao Zheng, Wanli Ouyang, Chunfeng Song

abstract:
Human's perception of the visual world is shaped by the stereo processing of 3D information. Understanding how the brain perceives and processes 3D visual stimuli in the real world has been a longstanding endeavor in neuroscience. Towards this goal, we introduce a new neuroscience task: decoding 3D visual perception from EEG signals, a neuroimaging technique that enables real-time monitoring of neural dynamics enriched with complex visual cues. To provide the essential benchmark, we first present EEG-3D, a pioneering dataset featuring multimodal analysis data and extensive EEG recordings from 12 subjects viewing 72 categories of 3D objects rendered in both videos and images. Furthermore, we propose Neuro-3D, a 3D visual decoding framework based on EEG signals. This framework adaptively integrates EEG features derived from static and dynamic stimuli to learn complementary and robust neural representations, which are subsequently utilized to recover both the shape and color of 3D objects through the proposed diffusion-based colored point cloud decoder. To the best of our knowledge, we are the first to explore EEG-based 3D visual decoding. Experiments indicate that Neuro-3D not only reconstructs colored 3D objects with high fidelity,  but also learns effective neural representations that enable insightful brain region analysis. The code and dataset are available at https://github.com/gzq17/neuro-3D.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Neuro-3D_Towards_3D_Visual_Decoding_from_EEG_Signals_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_Neuro-3D_Towards_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Zhanqiang and Wu, Jiamin and Song, Yonghao and Bu, Jiahui and Mai, Weijian and Zheng, Qihao and Ouyang, Wanli and Song, Chunfeng},
    title     = {Neuro-3D: Towards 3D Visual Decoding from EEG Signals},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23870-23880}
}

raw_affiliation:
[{"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shanghai Innovation Institute", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}]

affiliations:
["Shanghai Artificial Intelligence Laboratory", "Tsinghua University", "The Chinese University of Hong Kong", "Shanghai Innovation Institute", "Shanghai Jiao Tong University", "South China University of Technology"]


title: FastVLM: Efficient Vision Encoding for Vision Language Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19769-19780
raw_affiliation: [{"affiliation": "Apple", "city": "Cupertino", "country": "USA"}]
affiliations: ["Apple"]
cities: ["Cupertino"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Vasu_FastVLM_Efficient_Vision_Encoding_for_Vision_Language_Models_CVPR_2025_paper.html

authors:
Pavan Kumar Anasosalu Vasu, Fartash Faghri, Chun-Liang Li, Cem Koc, Nate True, Albert Antony, Gokula Santhanam, James Gabriel, Peter Grasch, Oncel Tuzel, Hadi Pouransari

abstract:
Vision Language Models (VLMs) like LLaVA encode images into tokens aligned to the word embedding space of the LLM decoder. Scaling input image resolution is essential for improving performance, especially in text-rich image understanding tasks. However, popular visual encoders such as CLIP-pretrained ViTs become inefficient at high resolutions due to the large number of tokens and high encoding latency caused by stacked self-attention layers. At different operational resolutions, the vision encoder of a VLM can be optimized along two axes: reducing encoding latency and minimizing the number of visual tokens passed to the LLM, thereby lowering overall latency. In this work, we introduce FastVLM, which achieves an optimized trade-off between resolution, latency, and accuracy by incorporating FastViTHD--a new hybrid vision encoder that outputs fewer tokens and significantly reduces encoding time while processing high-resolution images. We provide a comprehensive efficiency analysis of the interplay between image resolution, vision latency, number of visual tokens, and LLM size. In the LLaVA-1.5 setup, we achieve 3.2x improvement in overall time-to-first-token (TTFT) while maintaining similar performance on VLM benchmarks compared to prior works. On text-rich evaluations like TextVQA and DocVQA, FastVLM obtains +8.4% and +12.5% better accuracy than ConvLLaVA at a similar operating point of 144 visual tokens. Compared to LLaVa-OneVision at the highest resolution (1152 x 1152), FastVLM achieves comparable performance on key benchmarks like SeedBench and MMMU, using the same LLM, but with 85x faster TTFT, 3x less vision instruction tuning data, and a vision encoder that is 3.4x smaller.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Vasu_FastVLM_Efficient_Vision_Encoding_for_Vision_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Vasu_FastVLM_Efficient_Vision_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Vasu_2025_CVPR,
    author    = {Vasu, Pavan Kumar Anasosalu and Faghri, Fartash and Li, Chun-Liang and Koc, Cem and True, Nate and Antony, Albert and Santhanam, Gokula and Gabriel, James and Grasch, Peter and Tuzel, Oncel and Pouransari, Hadi},
    title     = {FastVLM: Efficient Vision Encoding for Vision Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19769-19780}
}


title: VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20863-20873
cities: ["Santa Clara", "Little Rock", "Bethesda", "Oxford"]
countries: ["USA", "USA", "USA", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/He_VISTA3D_A_Unified_Segmentation_Foundation_Model_For_3D_Medical_Imaging_CVPR_2025_paper.html

authors:
Yufan He, Pengfei Guo, Yucheng Tang, Andriy Myronenko, Vishwesh Nath, Ziyue Xu, Dong Yang, Can Zhao, Benjamin Simon, Mason Belue, Stephanie Harmon, Baris Turkbey, Daguang Xu, Wenqi Li

abstract:
Foundation models for interactive segmentation in 2D natural images and videos have sparked significant interest in building 3D foundation models for medical imaging. However, the domain gaps and clinical use cases for 3D medical imaging require a dedicated model that diverges from existing 2D solutions. Specifically, such foundation models should support a full workflow that can actually reduce human effort. Treating 3D medical images as sequences of 2D slices and reusing interactive 2D foundation models seems straightforward, but 2D annotation is too time-consuming for 3D tasks. Moreover, for large cohort analysis, it's the highly accurate automatic segmentation models that reduce the most human effort. However, these models lack support for interactive corrections and lack zero-shot ability for novel structures, which is a key feature of "foundation". While reusing pre-trained 2D backbones in 3D enhances zero-shot potential, their performance on complex 3D structures still lags behind leading 3D models. To address these issues, we present VISTA3D, Versatile Imaging SegmenTation and Annotation model, that targets to solve all thesechallenges and requirements with one unified foundation model. VISTA3D is built on top of the well-established 3D segmentation pipeline, and it is the first model to achieve state-of-the-art performance in both 3D automatic (supporting 127 classes) and 3D interactive segmentation, even when compared with top 3D expert models on large and diverse benchmarks. Additionally, VISTA3D's 3D interactive design allows efficient human correction, and a novel 3D supervoxel method that distills 2D pretrained backbones grants VISTA3D top 3D zero-shot performance. We believe the model, recipe, and insights represent a promising step toward a clinically useful 3D foundation model. Code and weights are publicly available at https://github.com/Project-MONAI/VISTA

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/He_VISTA3D_A_Unified_Segmentation_Foundation_Model_For_3D_Medical_Imaging_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/He_VISTA3D_A_Unified_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{He_2025_CVPR,
    author    = {He, Yufan and Guo, Pengfei and Tang, Yucheng and Myronenko, Andriy and Nath, Vishwesh and Xu, Ziyue and Yang, Dong and Zhao, Can and Simon, Benjamin and Belue, Mason and Harmon, Stephanie and Turkbey, Baris and Xu, Daguang and Li, Wenqi},
    title     = {VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20863-20873}
}

raw_affiliation:
[{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "University of Arkansas for Medical Sciences", "city": "Little Rock", "country": "USA"}, {"affiliation": "National Institutes of Health", "city": "Bethesda", "country": "USA"}, {"affiliation": "University of Oxford", "city": "Oxford", "country": "UK"}]

affiliations:
["NVIDIA", "University of Arkansas for Medical Sciences", "National Institutes of Health", "University of Oxford"]


title: VideoGigaGAN: Towards Detail-rich Video Super-Resolution
authors: Yiran Xu, Taesung Park, Richard Zhang, Yang Zhou, Eli Shechtman, Feng Liu, Jia-Bin Huang, Difan Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2139-2149
affiliations: ["Adobe Research", "University of Maryland, College Park"]
cities: ["San Jose", "College Park"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_VideoGigaGAN_Towards_Detail-rich_Video_Super-Resolution_CVPR_2025_paper.html

abstract:
Video super-resolution (VSR) models achieve temporal consistency but often produce blurrier results than their image-based counterparts due to limited generative capacity. This prompts the question: can we adapt a generative image upsampler for VSR while preserving temporal consistency? We introduce VideoGigaGAN, a new generative VSR model that combines high-frequency detail with temporal stability, building on the large-scale GigaGAN image upsampler. Simple adaptations of GigaGAN for VSR led to flickering issues, so we propose techniques to enhance temporal consistency. We validate the effectiveness of VideoGigaGAN by comparing it with state-of-the-art VSR models on public datasets and showcasing video results with 8x upsampling.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_VideoGigaGAN_Towards_Detail-rich_Video_Super-Resolution_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_VideoGigaGAN_Towards_Detail-rich_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Yiran and Park, Taesung and Zhang, Richard and Zhou, Yang and Shechtman, Eli and Liu, Feng and Huang, Jia-Bin and Liu, Difan},
    title     = {VideoGigaGAN: Towards Detail-rich Video Super-Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2139-2149}
}

raw_affiliation:
[{"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}, {"affiliation": "University of Maryland, College Park", "city": "College Park", "country": "USA"}]


title: Probing the Mid-level Vision Capabilities of Self-Supervised Learning
authors: Xuweiyi Chen, Markus Marks, Zezhou Cheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30095-30105
affiliations: ["University of Virginia", "California Institute of Technology"]
cities: ["Charlottesville", "Pasadena"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Probing_the_Mid-level_Vision_Capabilities_of_Self-Supervised_Learning_CVPR_2025_paper.html

abstract:
Mid-level vision capabilities -- such as generic object localization and 3D geometric understanding -- are not only fundamental to human vision but are also crucial for many real-world applications of computer vision. These abilities emerge with minimal supervision during the early stages of human visual development. Despite their significance, current self-supervised learning (SSL) approaches are primarily designed and evaluated for high-level recognition tasks, leaving their mid-level vision capabilities largely unexamined. In this study, we introduce a suite of benchmark protocols to systematically assess mid-level vision capabilities and present a comprehensive, controlled evaluation of 22 prominent SSL models across 8 mid-level vision tasks. Our experiments reveal a weak correlation between mid-level and high-level task performance. We also identify several SSL methods with highly imbalanced performance across mid-level and high-level capabilities, as well as some that excel in both. Additionally, we investigate key factors contributing to mid-level vision performance, such as pretraining objectives and network architectures. Our study provides a holistic and timely view of what SSL models have learned, complementing existing research that primarily focuses on high-level vision tasks. We hope our findings guide future SSL research to benchmark models not only on high-level vision tasks but on mid-level as well.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Probing_the_Mid-level_Vision_Capabilities_of_Self-Supervised_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Probing_the_Mid-level_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Xuweiyi and Marks, Markus and Cheng, Zezhou},
    title     = {Probing the Mid-level Vision Capabilities of Self-Supervised Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30095-30105}
}

raw_affiliation:
[{"affiliation": "University of Virginia", "city": "Charlottesville", "country": "USA"}, {"affiliation": "California Institute of Technology", "city": "Pasadena", "country": "USA"}]


title: S2D-LFE: Sparse-to-Dense Light Field Event Generation
authors: Yutong Liu, Wenming Weng, Yueyi Zhang, Zhiwei Xiong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11207-11216
affiliations: ["University of Science and Technology of China"]
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_S2D-LFE_Sparse-to-Dense_Light_Field_Event_Generation_CVPR_2025_paper.html

abstract:
In this paper, we present S2D-LFE, an innovative approach for sparse-to-dense light field event generation. For the first time to our knowledge, S2D-LFE enables controllable novel view synthesis only from sparse-view light field event (LFE) data, and addresses three critical challenges for the LFE generation task: simplicity, controllability, and consistency. The simplicity aspect eliminates the dependency on frame-based modality, which often suffers from motion blur and low frame-rate limitations. The controllability aspect enables precise view synthesis under sparse LFE conditions with view-related constraints. The consistency aspect ensures both cross-view and temporal coherence in the generated results. To realize S2D-LFE, we develop a novel diffusion-based generation network with two key components. First, we design an LFE-customized variational auto-encoder that effectively compresses and reconstructs LFE by integrating cross-view information. Second, we design an LFE-aware injection adaptor to extract comprehensive geometric and texture priors. Furthermore, we construct a large-scale synthetic LFE dataset containing 162 one-minute sequences using simulator, and capture a real-world testset using our custom-built sparse LFE acquisition system, covering diverse indoor and outdoor scenes. Extensive experiments demonstrate that S2D-LFE successfully generates up to 9x9 dense LFE from sparse 2x2 inputs and outperforms existing methods on both synthetic and real-world data. The datasets and code are available at https://github.com/Yutong2022/S2D-LFE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_S2D-LFE_Sparse-to-Dense_Light_Field_Event_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_S2D-LFE_Sparse-to-Dense_Light_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yutong and Weng, Wenming and Zhang, Yueyi and Xiong, Zhiwei},
    title     = {S2D-LFE: Sparse-to-Dense Light Field Event Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11207-11216}
}


title: The Art of Deception: Color Visual Illusions and Diffusion Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18642-18652
cities: ["Barcelona", "Barcelona", "Valencia", "Warsaw"]
countries: ["Spain", "Spain", "Spain", "Poland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gomez-Villa_The_Art_of_Deception_Color_Visual_Illusions_and_Diffusion_Models_CVPR_2025_paper.html

authors:
Alexandra Gomez-Villa, Kai Wang, C.Alejandro Parraga, Bartłomiej Twardowski, Jesus Malo, Javier Vazquez-Corral, Joost van den Weijer

abstract:
Visual illusions in humans arise when interpreting out-of-distribution stimuli: if the observer is adapted to certain statistics, perception of outliers deviates from reality. Recent studies have shown that artificial neural networks (ANNs) can also be deceived by visual illusions.This revelation raises profound questions about the nature of visual information. Why are two independent systems, both human brains and ANNs, susceptible to the same illusions? Should any ANN be capable of perceiving visual illusions? Are these perceptions a feature or a flaw? In this work, we study how visual illusions are encoded in diffusion models. Remarkably, we show that they present human-like brightness/color shifts in their latent space. We use this fact to demonstrate that diffusion models can predict visual illusions.  Furthermore, we also show how to generate new unseen visual illusions in realistic images using text-to-image diffusion models.  We validate this ability through psychophysical experiments that show how our model-generated illusions also fool humans.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gomez-Villa_The_Art_of_Deception_Color_Visual_Illusions_and_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gomez-Villa_The_Art_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gomez-Villa_2025_CVPR,
    author    = {Gomez-Villa, Alexandra and Wang, Kai and Parraga, C.Alejandro and Twardowski, Bart{\l}omiej and Malo, Jesus and Vazquez-Corral, Javier and van den Weijer, Joost},
    title     = {The Art of Deception: Color Visual Illusions and Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18642-18652}
}

raw_affiliation:
[{"affiliation": "Computer Vision Center", "city": "Barcelona", "country": "Spain"}, {"affiliation": "Universitat Aut`onoma de Barcelona", "city": "Barcelona", "country": "Spain"}, {"affiliation": "Universitat de Val`encia", "city": "Valencia", "country": "Spain"}, {"affiliation": "IDEAS-NCBR", "city": "Warsaw", "country": "Poland"}]

affiliations:
["Computer Vision Center", "Universitat Aut`onoma de Barcelona", "Universitat de Val`encia", "IDEAS-NCBR"]


title: GLUS: Global-Local Reasoning Unified into A Single Large Language Model for Video Segmentation
authors: Lang Lin, Xueyang Yu, Ziqi Pang, Yu-Xiong Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8658-8667
raw_affiliation: [{"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana", "country": "USA"}]
affiliations: ["University of Illinois Urbana-Champaign"]
cities: ["Urbana"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_GLUS_Global-Local_Reasoning_Unified_into_A_Single_Large_Language_Model_CVPR_2025_paper.html

abstract:
This paper proposes a novel framework utilizing multi-modal large language models (MLLMs) for referring video object segmentation (RefVOS). Previous MLLM-based methods commonly struggle with the dilemma between "Ref" and "VOS": they either specialize in understanding a few key frames (global reasoning) or tracking objects on continuous frames (local reasoning), and rely on external VOS or frame selectors to mitigate the other end of the challenge. However, our framework GLUS shows that Global and Local consistency can be Unified into a single video Segmentation MLLM: a set of sparse "context frames" provides global information, while a stream of continuous "query frames" conducts local object tracking. This is further supported by jointly training the MLLM with a pre-trained VOS memory bank to simultaneously digest short-range and long-range temporal information. To improve the information efficiency within the limited context window of MLLMs, we introduce object contrastive learning to distinguish hard false-positive objects and a self-refined framework to identify crucial frames and perform propagation. By collectively integrating these insights, our GLUS delivers a simple yet effective baseline, achieving new state-of-the-art for MLLMs on the MeViS and Ref-Youtube-VOS benchmark.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_GLUS_Global-Local_Reasoning_Unified_into_A_Single_Large_Language_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_GLUS_Global-Local_Reasoning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Lang and Yu, Xueyang and Pang, Ziqi and Wang, Yu-Xiong},
    title     = {GLUS: Global-Local Reasoning Unified into A Single Large Language Model for Video Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8658-8667}
}


authors: Zhiyuan Ma, Xinyue Liang, Rongyuan Wu, Xiangyu Zhu, Zhen Lei, Lei Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11036-11050
cities: ["Hong Kong", "Hong Kong", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Progressive_Rendering_Distillation_Adapting_Stable_Diffusion_for_Instant_Text-to-Mesh_Generation_CVPR_2025_paper.html

title:
Progressive Rendering Distillation: Adapting Stable Diffusion for Instant Text-to-Mesh Generation without 3D Data

abstract:
It is highly desirable to obtain a model that can generate high-quality 3D meshes from text prompts in just seconds. While recent attempts have adapted pre-trained text-to-image diffusion models, such as Stable Diffusion (SD), into generators of 3D representations (e.g., Triplane), they often suffer from poor quality due to the lack of sufficient high-quality 3D training data. Aiming at overcoming the data shortage, we propose a novel training scheme, termed as Progressive Rendering Distillation (PRD), eliminating the need for 3D ground-truths by distilling multi-view diffusion models and adapting SD into a native 3D generator. In each iteration of training, PRD uses the U-Net to progressively denoise the latent from random noise for a few steps, and in each step it decodes the denoised latent into 3D output. Multi-view diffusion models, including MVDream and RichDreamer, are used in joint with SD to distill text-consistent textures and geometries into the 3D outputs through score distillation. Since PRD supports training without 3D ground-truths, we can easily scale up the training data and improve generation quality for challenging text prompts with creative concepts. Meanwhile, PRD can accelerate the inference speed of the generation model in just a few steps.  With PRD, we train a Triplane generator, namely TriplaneTurbo, which adds only 2.5% trainable parameters to adapt SD for Triplane generation. TriplaneTurbo outperforms previous text-to-3D  generators in both efficiency and quality. Specifically, it can produce high-quality 3D meshes in 1.2 seconds and generalize well for challenging text input. The code is available at https://github.com/theEricMa/TriplaneTurbo.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_Progressive_Rendering_Distillation_Adapting_Stable_Diffusion_for_Instant_Text-to-Mesh_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_Progressive_Rendering_Distillation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Zhiyuan and Liang, Xinyue and Wu, Rongyuan and Zhu, Xiangyu and Lei, Zhen and Zhang, Lei},
    title     = {Progressive Rendering Distillation: Adapting Stable Diffusion for Instant Text-to-Mesh Generation without 3D Data},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11036-11050}
}

raw_affiliation:
[{"affiliation": "The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}, {"affiliation": "Center for Artificial Intelligence and Robotics, HKISI CAS", "city": "Hong Kong", "country": "China"}, {"affiliation": "State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, University of Chinese Academy of Sciences, UCAS", "city": "Beijing", "country": "China"}]

affiliations:
["The Hong Kong Polytechnic University", "Center for Artificial Intelligence and Robotics, HKISI CAS", "State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA", "School of Artificial Intelligence, University of Chinese Academy of Sciences, UCAS"]


title: Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction
authors: Huiwon Jang, Sihyun Yu, Jinwoo Shin, Pieter Abbeel, Younggyo Seo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22853-22863
affiliations: ["KAIST", "UC Berkeley"]
cities: ["Daejeon", "Berkeley"]
countries: ["South Korea", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jang_Efficient_Long_Video_Tokenization_via_Coordinate-based_Patch_Reconstruction_CVPR_2025_paper.html

abstract:
Efficient tokenization of videos remains a challenge in training vision models that can process long videos. One promising direction is to develop a tokenizer that can encode long video clips, as it would enable the tokenizer to leverage the temporal coherence of videos better for tokenization. However, training existing tokenizers on long videos often incurs a huge training cost as they are trained to reconstruct all the frames at once. In this paper, we introduce CoordTok, a video tokenizer that learns a mapping from coordinate-based representations to the corresponding patches of input videos, inspired by recent advances in 3D generative models. In particular, CoordTok encodes a video into factorized triplane representations and reconstructs patches that correspond to randomly sampled (x,y,t) coordinates. This allows for training large tokenizer models directly on long videos without requiring excessive training resources. Our experiments show that CoordTok can drastically reduce the number of tokens for encoding long video clips. For instance, CoordTok can encode a 128-frame video with 128x128 resolution into 1280 tokens, while baselines need 6144 or 8192 tokens to achieve similar reconstruction quality. We further show that this efficient video tokenization enables memory-efficient training of a diffusion transformer that can generate 128 frames at once.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jang_Efficient_Long_Video_Tokenization_via_Coordinate-based_Patch_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jang_Efficient_Long_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jang_2025_CVPR,
    author    = {Jang, Huiwon and Yu, Sihyun and Shin, Jinwoo and Abbeel, Pieter and Seo, Younggyo},
    title     = {Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22853-22863}
}

raw_affiliation:
[{"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}]


title: Derivative-Free Diffusion Manifold-Constrained Gradient for Unified XAI
authors: Won Jun Kim, Hyungjin Chung, Jaemin Kim, Sangmin Lee, Byeongsu Sim, Jong Chul Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23795-23805
affiliations: ["KAIST", "EverEx"]
cities: ["Daejeon", "Seoul"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Derivative-Free_Diffusion_Manifold-Constrained_Gradient_for_Unified_XAI_CVPR_2025_paper.html

abstract:
Gradient-based methods are a prototypical family of "explainability for AI" (XAI) techniques, especially for image-based models. However, they (1) require white-box access to models, (2) are vulnerable to adversarial attacks, and (3) produce attributions that lie off the image manifold, leading to explanations that are not amenable to human perception. To overcome these challenges, we introduce Derivative-Free Diffusion Manifold-Contrained Gradients (FreeMCG): by leveraging ensemble Kalman filters and diffusion models, we derive a derivative-free approximation of the model's gradient projected onto the data manifold, requiring access only to the model's outputs (i.e., black-box setting). We demonstrate the effectiveness of FreeMCG by applying it to both counterfactual generation and feature attribution, which have traditionally been treated as different tasks requiring distinct methods. Through comprehensive evaluation on both counterfactual explanation and feature attribution we show that our method yields state-of-the-art results for both tasks while preserving the essential properties expected of XAI tools. Code: https://github.com/one-june/FreeMCG.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Derivative-Free_Diffusion_Manifold-Constrained_Gradient_for_Unified_XAI_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Derivative-Free_Diffusion_Manifold-Constrained_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Won Jun and Chung, Hyungjin and Kim, Jaemin and Lee, Sangmin and Sim, Byeongsu and Ye, Jong Chul},
    title     = {Derivative-Free Diffusion Manifold-Constrained Gradient for Unified XAI},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23795-23805}
}

raw_affiliation:
[{"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "EverEx", "city": "Seoul", "country": "South Korea"}]


title: ZoomLDM: Latent Diffusion Model for Multi-scale Image Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23453-23463
raw_affiliation: [{"affiliation": "Stony Brook University", "city": "Stony Brook", "country": "USA"}]
affiliations: ["Stony Brook University"]
cities: ["Stony Brook"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yellapragada_ZoomLDM_Latent_Diffusion_Model_for_Multi-scale_Image_Generation_CVPR_2025_paper.html

authors:
Srikar Yellapragada, Alexandros Graikos, Kostas Triaridis, Prateek Prasanna, Rajarsi Gupta, Joel Saltz, Dimitris Samaras

abstract:
Diffusion models have revolutionized image generation, yet several challenges restrict their application to large-image domains, such as digital pathology and satellite imagery. Given that it is infeasible to directly train a model on 'whole' images from domains with potential gigapixel sizes, diffusion-based generative methods have focused on synthesizing small, fixed-size patches extracted from these images. However, generating small patches has limited applicability since patch-based models fail to capture the global structures and wider context of large images, which can be crucial for synthesizing (semantically) accurate samples. To overcome this limitation, we present ZoomLDM, a diffusion model tailored for generating images across multiple scales. Central to our approach is a novel magnification-aware conditioning mechanism that utilizes self-supervised learning (SSL) embeddings and allows the diffusion model to synthesize images at different 'zoom' levels, i.e., fixed-size patches extracted from large images at varying scales. ZoomLDM synthesizes coherent histopathology images that remain contextually accurate and detailed at different zoom levels, achieving state-of-the-art image generation quality across all scales and excelling in the data-scarce setting of generating thumbnails of entire large images. The multi-scale nature of ZoomLDM unlocks additional capabilities in large image generation, enabling computationally tractable and globally coherent image synthesis up to 4096x4096 pixels and 4x super-resolution. Additionally, multi-scale features extracted from ZoomLDM are highly effective in multiple instance learning experiments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yellapragada_ZoomLDM_Latent_Diffusion_Model_for_Multi-scale_Image_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yellapragada_ZoomLDM_Latent_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yellapragada_2025_CVPR,
    author    = {Yellapragada, Srikar and Graikos, Alexandros and Triaridis, Kostas and Prasanna, Prateek and Gupta, Rajarsi and Saltz, Joel and Samaras, Dimitris},
    title     = {ZoomLDM: Latent Diffusion Model for Multi-scale Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23453-23463}
}


authors: Yancheng Cai, Fei Yin, Dounia Hammou, Rafal Mantiuk
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20039-20048
raw_affiliation: [{"affiliation": "University of Cambridge", "city": "Cambridge", "country": "UK"}]
affiliations: ["University of Cambridge"]
cities: ["Cambridge"]
countries: ["UK"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cai_Do_Computer_Vision_Foundation_Models_Learn_the_Low-level_Characteristics_of_CVPR_2025_paper.html

title:
Do Computer Vision Foundation Models Learn the Low-level Characteristics of the Human Visual System?

abstract:
Computer vision foundation models, such as DINO or OpenCLIP, are trained in a self-supervised manner on large image datasets. Analogously, substantial evidence suggests that the human visual system (HVS) is influenced by the statistical distribution of colors and patterns in the natural world, characteristics also present in the training data of foundation models. The question we address in this paper is whether foundation models trained on natural images mimic some of the low-level characteristics of the human visual system, such as contrast detection, contrast masking, and contrast constancy. Specifically, we designed a protocol comprising nine test types to evaluate the image encoders of 45 foundation and generative models. Our results indicate that some foundation models (e.g., DINO, DINOv2, and OpenCLIP), share some of the characteristics of human vision, but other models show little resemblance. Foundation models tend to show smaller sensitivity to low contrast and rather irregular responses to contrast across frequencies. The foundation models show the best agreement with human data in terms of contrast masking. Our findings suggest that human vision and computer vision may take both similar and different paths when learning to interpret images of the real world. Overall, while differences remain, foundation models trained on vision tasks start to align with low-level human vision, with DINOv2 showing the closest resemblance. Our code is available on https://github.com/caiyancheng/VFM_HVS_CVPR2025.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cai_Do_Computer_Vision_Foundation_Models_Learn_the_Low-level_Characteristics_of_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cai_Do_Computer_Vision_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Cai_2025_CVPR,
    author    = {Cai, Yancheng and Yin, Fei and Hammou, Dounia and Mantiuk, Rafal},
    title     = {Do Computer Vision Foundation Models Learn the Low-level Characteristics of the Human Visual System?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20039-20048}
}


title: GaussianUDF: Inferring Unsigned Distance Functions through 3D Gaussian Splatting
authors: Shujuan Li, Yu-Shen Liu, Zhizhong Han
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27113-27123
cities: ["Beijing", "Detroit"]
countries: ["China", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_GaussianUDF_Inferring_Unsigned_Distance_Functions_through_3D_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
Reconstructing open surfaces from multi-view images is vital in digitalizing complex objects in daily life. A widely used strategy is to learn unsigned distance functions (UDFs) by checking if their appearance conforms to the image observations through neural rendering. However, it is still hard to learn continuous and implicit UDF representations through 3D Gaussians splatting (3DGS) due to the discrete and explicit scene representation, i.e., 3D Gaussians. To resolve this issue, we propose a novel approach to bridge the gap between 3D Gaussians and UDFs. Our key idea is to overfit thin and flat 2D Gaussian planes on surfaces, and then, leverage the self-supervision and gradient-based inference to supervise unsigned distances in both near and far area to surfaces. To this end, we introduce novel constraints and strategies to constrain the learning of 2D Gaussians to pursue more stable optimization and more reliable self-supervision, addressing the challenges brought by complicated gradient field on or near the zero level set of UDFs. We report numerical and visual comparisons with the state-of-the-art on widely used benchmarks and real data to show our advantages in terms of accuracy, efficiency, completeness, and sharpness of reconstructed open surfaces with boundaries.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_GaussianUDF_Inferring_Unsigned_Distance_Functions_through_3D_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_GaussianUDF_Inferring_Unsigned_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Shujuan and Liu, Yu-Shen and Han, Zhizhong},
    title     = {GaussianUDF: Inferring Unsigned Distance Functions through 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27113-27123}
}

raw_affiliation:
[{"affiliation": "School of Software, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Department of Computer Science, Wayne State University", "city": "Detroit", "country": "USA"}]

affiliations:
["School of Software, Tsinghua University", "Department of Computer Science, Wayne State University"]


