# papers

authors: Bohan Xiao, Peiyong Wang, Qisheng He, Ming Dong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28232-28241
cities: ["Detroit", "Detroit"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_Deterministic_Image-to-Image_Translation_via_Denoising_Brownian_Bridge_Models_with_Dual_CVPR_2025_paper.html

title:
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual Approximators

abstract:
Image-to-Image (I2I) translation involves converting an im- age from one domain to another. Deterministic I2I transla- tion, such as in image super-resolution, extends this con- cept by guaranteeing that each input generates a consistent and predictable output, closely matching the ground truth (GT) with high fidelity. In this paper, we propose a denois- ing Brownian bridge model with dual approximators (Dual- approx Bridge), a novel generative model that exploits the Brownian bridge dynamics and two neural network-based approximators (one for forward and one for reverse pro- cess) to produce faithful output with negligible variance and high image quality in I2I translations. Our extensive exper- iments on benchmark datasets including image generation and super-resolution demonstrate the consistent and supe- rior performance of Dual-approx Bridge in terms of im- age quality and faithfulness to GT when compared to both stochastic and deterministic baselines. Project page and code: https://github.com/bohan95/dual-app-bridge

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_Deterministic_Image-to-Image_Translation_via_Denoising_Brownian_Bridge_Models_with_Dual_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Xiao_2025_CVPR,
    author    = {Xiao, Bohan and Wang, Peiyong and He, Qisheng and Dong, Ming},
    title     = {Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual Approximators},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28232-28241}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science, Wayne State University 1", "city": "Detroit", "country": "USA"}, {"affiliation": "Department of Mathematics, Wayne State University2", "city": "Detroit", "country": "USA"}]

affiliations:
["Department of Computer Science, Wayne State University 1", "Department of Mathematics, Wayne State University2"]


title: Towards Source-Free Machine Unlearning
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4948-4957
affiliations: ["Brookhaven National Laboratory", "University of California, Riverside", "AWS AI"]
cities: ["Upton", "Riverside", "Seattle"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ahmed_Towards_Source-Free_Machine_Unlearning_CVPR_2025_paper.html

authors:
Sk Miraj Ahmed, Umit Yigit Basaran, Dripta S. Raychaudhuri, Arindam Dutta, Rohit Kundu, Fahim Faisal Niloy, Basak Guler, Amit K. Roy-Chowdhury

abstract:
As machine learning become more pervasive and data privacy regulations evolve, the ability to remove private or copyrighted information from trained models is becoming an increasingly critical requirement. Existing unlearning methods often rely on the assumption of having access to the entire training dataset during the forgetting process. However, this assumption may not hold true in practical scenarios where the original training data may not be accessible, i.e., the source-free setting. To address this challenge, we focus on the source-free unlearning scenario, where an unlearning algorithm must be capable of removing specific data from a trained model without requiring access to the original training dataset. Building on recent work, we present a method that can estimate the Hessian of the unknown remaining training data, a crucial component required for efficient unlearning. Leveraging this estimation technique, our method enables efficient zero-shot unlearning while providing robust theoretical guarantees on the unlearning performance, while maintaining performance on the remaining data. Extensive experiments over a wide range of datasets verify the efficacy of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ahmed_Towards_Source-Free_Machine_Unlearning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ahmed_Towards_Source-Free_Machine_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ahmed_2025_CVPR,
    author    = {Ahmed, Sk Miraj and Basaran, Umit Yigit and Raychaudhuri, Dripta S. and Dutta, Arindam and Kundu, Rohit and Niloy, Fahim Faisal and Guler, Basak and Roy-Chowdhury, Amit K.},
    title     = {Towards Source-Free Machine Unlearning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4948-4957}
}

raw_affiliation:
[{"affiliation": "Brookhaven National Laboratory", "city": "Upton", "country": "USA"}, {"affiliation": "University of California, Riverside", "city": "Riverside", "country": "USA"}, {"affiliation": "AWS AI", "city": "Seattle", "country": "USA"}]


title: Uni4D: Unifying Visual Foundation Models for 4D Modeling from a Single Video
authors: David Yifan Yao, Albert J. Zhai, Shenlong Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1116-1126
raw_affiliation: [{"affiliation": "University of Illinois at Urbana-Champaign", "city": "Urbana", "country": "USA"}]
affiliations: ["University of Illinois at Urbana-Champaign"]
cities: ["Urbana"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yao_Uni4D_Unifying_Visual_Foundation_Models_for_4D_Modeling_from_a_CVPR_2025_paper.html

abstract:
This paper presents a unified approach to understanding dynamic scenes from casual videos. Large pretrained vision foundation models, such as vision-language, video depth prediction, motion tracking, and segmentation models, offer promising capabilities. However, training a single model for comprehensive 4D understanding remains challenging. We introduce Uni4D, a multi-stage optimization framework that harnesses multiple pretrained models to advance dynamic 3D modeling, including static/dynamic reconstruction, camera pose estimation, and dense 3D motion tracking. Our results show state-of-the-art performance in dynamic 4D modeling with superior visual quality. Notably, Uni4D requires no retraining or fine-tuning, highlighting the effectiveness of repurposing visual foundation models for 4D understanding. Code and more results are available at: https://davidyao99.github.io/uni4d.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yao_Uni4D_Unifying_Visual_Foundation_Models_for_4D_Modeling_from_a_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yao_Uni4D_Unifying_Visual_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yao_2025_CVPR,
    author    = {Yao, David Yifan and Zhai, Albert J. and Wang, Shenlong},
    title     = {Uni4D: Unifying Visual Foundation Models for 4D Modeling from a Single Video},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1116-1126}
}


title: DynScene: Scalable Generation of Dynamic Robotic Manipulation Scenes for Embodied AI
authors: Sangmin Lee, Sungyong Park, Heewon Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12166-12175
raw_affiliation: [{"affiliation": "Soongsil University", "city": "Seoul", "country": "South Korea"}]
affiliations: ["Soongsil University"]
cities: ["Seoul"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_DynScene_Scalable_Generation_of_Dynamic_Robotic_Manipulation_Scenes_for_Embodied_CVPR_2025_paper.html

abstract:
Robotic manipulation in embodied AI critically depends on large-scale, high-quality datasets that reflect realistic object interactions and physical dynamics. However, existing data collection pipelines are often slow, expensive, and heavily reliant on manual efforts. We present DynScene, a diffusion-based framework for generating dynamic robotic manipulation scenes directly from textual instructions. Unlike prior methods that focus solely on static environments or isolated robot actions, DynScene decomposes the generation into two phases static scene synthesis and action trajectory generation allowing fine-grained control and diversity. Our model enhances realism and physical feasibility through scene refinement (layout sampling, quaternion quantization) and leverages residual action representation to enable action augmentation, generating multiple diverse trajectories from a single static configuration. Experiments show DynScene achieves 26.8x faster generation, 1.84x higher accuracy, and 28% greater action diversity than human-crafted data. Furthermore, agents trained with DynScene exhibit up to 19.4% higher success rates across complex manipulation tasks. Our approach paves the way for scalable, automated dataset generation in robot learning.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_DynScene_Scalable_Generation_of_Dynamic_Robotic_Manipulation_Scenes_for_Embodied_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_DynScene_Scalable_Generation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Sangmin and Park, Sungyong and Kim, Heewon},
    title     = {DynScene: Scalable Generation of Dynamic Robotic Manipulation Scenes for Embodied AI},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12166-12175}
}


title: DiffLocks: Generating 3D Hair from a Single Image using Diffusion Models
authors: Radu Alexandru Rosu, Keyu Wu, Yao Feng, Youyi Zheng, Michael J. Black
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10847-10857
cities: ["Berlin", "Hangzhou", "Stanford", "T\u00fcbingen"]
countries: ["Germany", "China", "USA", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Rosu_DiffLocks_Generating_3D_Hair_from_a_Single_Image_using_Diffusion_CVPR_2025_paper.html

abstract:
We address the task of generating 3D hair geometry from a single image, which is challenging due to the diversity of hairstyles and the lack of paired image-to-3D hair data. Previous methods are primarily trained on synthetic data and cope with the limited amount of such data by using low-dimensional intermediate representations, such as guide strands and scalp-level embeddings, that require post-processing to decode, upsample, and add realism. These approaches fail to reconstruct detailed hair, struggle with curly hair, or are limited to handling only a few hairstyles. To overcome these limitations, we propose DiffLocks, a novel framework that enables detailed reconstruction of a wide variety of hairstyles directly from a single image. First, we address the lack of 3D hair data by automating the creation of the largest synthetic hair dataset to date, containing 40K hairstyles. Second, we leverage the synthetic hair dataset to learn an image-conditioned diffusion-transfomer model that generates accurate 3D strands from a single frontal image. By using a pretrained image backbone, our method generalizes to in-the-wild images despite being trained only on synthetic data. Our diffusion model predicts a scalp texture map in which any point in the map contains the latent code for an individual hair strand. These codes are directly decoded to 3D strands without post-processing techniques. Representing individual strands, instead of guide strands, enables the transformer to model the detailed spatial structure of complex hairstyles. With this, DiffLocks can recover highly curled hair, like afro hairstyles, from a single image for the first time. Data and code is available at https://radualexandru.github.io/difflocks

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Rosu_DiffLocks_Generating_3D_Hair_from_a_Single_Image_using_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rosu_DiffLocks_Generating_3D_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Rosu_2025_CVPR,
    author    = {Rosu, Radu Alexandru and Wu, Keyu and Feng, Yao and Zheng, Youyi and Black, Michael J.},
    title     = {DiffLocks: Generating 3D Hair from a Single Image using Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10847-10857}
}

raw_affiliation:
[{"affiliation": "Meshcapade", "city": "Berlin", "country": "Germany"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "Max Planck Institute for Intelligent Systems", "city": "T\u00fcbingen", "country": "Germany"}]

affiliations:
["Meshcapade", "Zhejiang University", "Stanford University", "Max Planck Institute for Intelligent Systems"]


title: Hyperbolic Category Discovery
authors: Yuanpei Liu, Zhenqi He, Kai Han
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9891-9900
affiliations: ["Visual AI Lab, The University of Hong Kong"]
cities: ["Hong Kong"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Hyperbolic_Category_Discovery_CVPR_2025_paper.html

abstract:
Generalized Category Discovery (GCD) is an intriguing open-world problem that has garnered increasing attention. Given a dataset that includes both labelled and unlabelled images, GCD aims to categorize all images in the unlabelled subset, regardless of whether they belong to known or unknown classes. In GCD, the common practice typically involves applying a spherical projection operator at the end of the self-supervised pretrained backbone, operating within Euclidean or spherical space. However, both of these spaces have been shown to be suboptimal for encoding samples that possess hierarchical structures. In contrast, hyperbolic space exhibits exponential volume growth relative to radius, making it inherently strong at capturing the hierarchical structure of samples from both seen and unseen categories. Therefore, we propose to tackle the category discovery challenge in the hyperbolic space. We introduce HypCD, a simple Hyperbolic framework for learning hierarchy-aware representations and classifiers for generalized Category Discovery. HypCD first transforms the Euclidean embedding space of the backbone network into hyperbolic space, facilitating subsequent representation and classification learning by considering both hyperbolic distance and the angle between samples. This approach is particularly helpful for knowledge transfer from known to unknown categories in GCD. We thoroughly evaluate HypCD on public GCD benchmarks, by applying it to various baseline and state-of-the-art methods, consistently achieving significant improvements.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Hyperbolic_Category_Discovery_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Hyperbolic_Category_Discovery_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yuanpei and He, Zhenqi and Han, Kai},
    title     = {Hyperbolic Category Discovery},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9891-9900}
}

raw_affiliation:
[{"affiliation": "Visual AI Lab, The University of Hong Kong", "city": "Hong Kong", "country": "China"}]


title: The Language of Motion: Unifying Verbal and Non-verbal Language of 3D Human Motion
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6200-6211
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/Chen_The_Language_of_Motion_Unifying_Verbal_and_Non-verbal_Language_of_CVPR_2025_paper.html

authors:
Changan Chen, Juze Zhang, Shrinidhi K. Lakshmikanth, Yusu Fang, Ruizhi Shao, Gordon Wetzstein, Li Fei-Fei, Ehsan Adeli

abstract:
Human communication is inherently multimodal, involving a combination of verbal and non-verbal cues such as speech, facial expressions, and body gestures. Modeling these behaviors is essential for understanding human interaction and for creating virtual characters that can communicate naturally in applications like games, films, and virtual reality. However, existing motion generation models are typically limited to specific input modalities--either speech, text, or motion data--and cannot fully leverage the diversity of available data. In this paper, we propose a novel framework that unifies verbal and non-verbal language using multimodal language models for human motion understanding and generation. This model is flexible in taking text, speech, and motion or any combination of them as input. Coupled with our novel pre-training strategy, our model not only achieves state-of-the-art performance on co-speech gesture generation but also requires much less data for training. Our model also unlocks an array of novel tasks such as editable gesture generation and emotion prediction from motion. We believe unifying the verbal and non-verbal language of human motion is essential for real-world applications, and language models offer a powerful approach to achieving this goal.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_The_Language_of_Motion_Unifying_Verbal_and_Non-verbal_Language_of_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_The_Language_of_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Changan and Zhang, Juze and Lakshmikanth, Shrinidhi K. and Fang, Yusu and Shao, Ruizhi and Wetzstein, Gordon and Fei-Fei, Li and Adeli, Ehsan},
    title     = {The Language of Motion: Unifying Verbal and Non-verbal Language of 3D Human Motion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6200-6211}
}


title: CALICO: Part-Focused Semantic Co-Segmentation with Large Vision-Language Models
authors: Kiet A. Nguyen, Adheesh Juvekar, Tianjiao Yu, Muntasir Wahed, Ismini Lourentzou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4550-4561
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/Nguyen_CALICO_Part-Focused_Semantic_Co-Segmentation_with_Large_Vision-Language_Models_CVPR_2025_paper.html

abstract:
Recent advances in Large Vision-Language Models (LVLMs) have enabled general-purpose vision tasks through visual instruction tuning. While existing LVLMs can generate segmentation masks from text prompts for single images, they struggle with segmentation-grounded reasoning across images, especially at finer granularities such as object parts. In this paper, we introduce the new task of part-focused semantic co-segmentation, which involves identifying and segmenting common objects and their constituent common and unique parts across images. To address this task, we present CALICO, the first LVLM designed for multi-image part-level reasoning segmentation. CALICO features two key components, a novel Correspondence Extraction Module that identifies semantic part-level correspondences, and Correspondence Adaptation Modules that embed this information into the LVLM to facilitate multi-image understanding in a parameter-efficient manner. To support training and evaluation, we curate MixedParts, a large-scale multi-image segmentation dataset containing  2.4M samples across  44K images spanning diverse object and part categories. Experimental results demonstrate that CALICO, with just 0.3% of its parameters finetuned, achieves strong performance on this challenging task.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nguyen_CALICO_Part-Focused_Semantic_Co-Segmentation_with_Large_Vision-Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nguyen_CALICO_Part-Focused_Semantic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nguyen_2025_CVPR,
    author    = {Nguyen, Kiet A. and Juvekar, Adheesh and Yu, Tianjiao and Wahed, Muntasir and Lourentzou, Ismini},
    title     = {CALICO: Part-Focused Semantic Co-Segmentation with Large Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4550-4561}
}


title: Task Preference Optimization: Improving Multimodal Large Language Models with Vision Task Alignment
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29880-29892
cities: ["Shanghai", "Hangzhou", "Hefei", "Shanghai", "Shenzhen", "Nanjing", "Shanghai"]
countries: ["China", "China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yan_Task_Preference_Optimization_Improving_Multimodal_Large_Language_Models_with_Vision_CVPR_2025_paper.html

authors:
Ziang Yan, Zhilin Li, Yinan He, Chenting Wang, Kunchang Li, Xinhao Li, Xiangyu Zeng, Zilei Wang, Yali Wang, Yu Qiao, Limin Wang, Yi Wang

abstract:
Current multimodal large language models (MLLMs) struggle with fine-grained or precise understanding of visuals although they give comprehensive perception and reasoning in a spectrum of vision applications. Recent studies either develop tool-using or unify specific visual tasks into the autoregressive framework, often at the expense of overall multimodal performance. To address this issue and enhance MLLMs with visual tasks in a scalable fashion, we propose Task Preference Optimization (TPO), a novel method that utilizes differentiable task preferences derived from typical fine-grained visual tasks. TPO introduces learnable task tokens that establish connections between multiple task-specific heads and the MLLM. By leveraging rich visual labels during training, TPO significantly enhances the MLLM's multimodal capabilities and task-specific performance. Through multi-task co-training within TPO, we observe synergistic benefits that elevate individual task performance beyond what is achievable through single-task training methodologies. Our instantiation of this approach with VideoChat and LLaVA demonstrates an overall 14.6% improvement in multimodal performance compared to baseline models. Additionally, MLLM-TPO demonstrates robust zero-shot capabilities across various tasks, performing comparably to state-of-the-art supervised models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Task_Preference_Optimization_Improving_Multimodal_Large_Language_Models_with_Vision_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_Task_Preference_Optimization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yan_2025_CVPR,
    author    = {Yan, Ziang and Li, Zhilin and He, Yinan and Wang, Chenting and Li, Kunchang and Li, Xinhao and Zeng, Xiangyu and Wang, Zilei and Wang, Yali and Qiao, Yu and Wang, Limin and Wang, Yi},
    title     = {Task Preference Optimization: Improving Multimodal Large Language Models with Vision Task Alignment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29880-29892}
}

raw_affiliation:
[{"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences", "city": "Shenzhen", "country": "China"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Shanghai Innovation Institute", "city": "Shanghai", "country": "China"}]

affiliations:
["Shanghai AI Laboratory", "Zhejiang University", "University of Science and Technology of China", "Shanghai Jiao Tong University", "Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences", "Nanjing University", "Shanghai Innovation Institute"]


title: Cross-modal Causal Relation Alignment for Video Question Grounding
authors: Weixing Chen, Yang Liu, Binglin Chen, Jiandong Su, Yongsen Zheng, Liang Lin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24087-24096
cities: ["Guangzhou", "Singapore", "Shenzhen", "Guangzhou"]
countries: ["China", "Singapore", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Cross-modal_Causal_Relation_Alignment_for_Video_Question_Grounding_CVPR_2025_paper.html

abstract:
Video question grounding (VideoQG) requires models to answer the questions and simultaneously infer the relevant video segments to support the answers. However, existing VideoQG methods usually suffer from spurious cross-modal correlations, leading to a failure to identify the dominant visual scenes that align with the intended question. Moreover, vision-language models exhibit unfaithful generalization performance and lack robustness on challenging downstream tasks such as VideoQG. In this work, we propose a novel VideoQG framework named Cross-modal Causal Relation Alignment (CRA), to eliminate spurious correlations and improve the causal consistency between question-answering and video temporal grounding. Our CRA involves three essential components: i) Gaussian Smoothing Grounding (GSG) module for estimating the time interval via cross-modal attention, which is de-noised by an adaptive Gaussian filter, ii) Cross-Modal Alignment (CMA) enhances the performance of weakly supervised VideoQG by leveraging bidirectional contrastive learning between estimated video segments and QA features, iii) Explicit Causal Intervention (ECI) module for multimodal deconfounding, which involves front-door intervention for vision and back-door intervention for language. Extensive experiments on two VideoQG datasets demonstrate the superiority of our CRA in discovering visually grounded content and achieving robust question reasoning. Codes are available at https://github.com/WissingChen/CRA-GQA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Cross-modal_Causal_Relation_Alignment_for_Video_Question_Grounding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Cross-modal_Causal_Relation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Weixing and Liu, Yang and Chen, Binglin and Su, Jiandong and Zheng, Yongsen and Lin, Liang},
    title     = {Cross-modal Causal Relation Alignment for Video Question Grounding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24087-24096}
}

raw_affiliation:
[{"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Guangdong Key Laboratory of Big Data Analysis and Processing", "city": "Guangzhou", "country": "China"}]

affiliations:
["Sun Yat-sen University", "Nanyang Technological University", "Peng Cheng Laboratory", "Guangdong Key Laboratory of Big Data Analysis and Processing"]


title: Words or Vision: Do Vision-Language Models Have Blind Faith in Text?
authors: Ailin Deng, Tri Cao, Zhirui Chen, Bryan Hooi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3867-3876
raw_affiliation: [{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]
affiliations: ["National University of Singapore"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Deng_Words_or_Vision_Do_Vision-Language_Models_Have_Blind_Faith_in_CVPR_2025_paper.html

abstract:
Vision-Language Models (VLMs) excel in integrating visual and textual information for vision-centric tasks, but their handling of inconsistencies between modalities is underexplored. We investigate VLMs' modality preferences when faced with visual data and varied textual inputs in vision-centered settings.By introducing textual variations to four vision-centric tasks and evaluating ten Vision-Language Models (VLMs), we discover a "blind faith in text" phenomenon: VLMs disproportionately trust textual data over visual data when inconsistencies arise, leading to significant performance drops under corrupted text and raising safety concerns.We analyze factors influencing this text bias, including instruction prompts, language model size, text relevance, token order, and the interplay between visual and textual certainty. While certain factors, such as scaling up the language model size, slightly mitigate text bias, others like token order can exacerbate it due to positional biases inherited from language models. To address this issue, we explore supervised fine-tuning with text augmentation and demonstrate its effectiveness in reducing text bias. Additionally, we provide a theoretical analysis suggesting that the blind faith in text phenomenon may stem from an imbalance of pure text and multi-modal data during training.Our findings highlight the need for balanced training and careful consideration of modality interactions in VLMs to enhance their robustness and reliability in handling multi-modal data inconsistencies.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Deng_Words_or_Vision_Do_Vision-Language_Models_Have_Blind_Faith_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Deng_Words_or_Vision_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Deng_2025_CVPR,
    author    = {Deng, Ailin and Cao, Tri and Chen, Zhirui and Hooi, Bryan},
    title     = {Words or Vision: Do Vision-Language Models Have Blind Faith in Text?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3867-3876}
}


title: Diffusion Renderer: Neural Inverse and Forward Rendering with Video Diffusion Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26069-26080
affiliations: ["NVIDIA", "University of Toronto", "Vector Institute", "University of Illinois Urbana-Champaign"]
cities: ["Santa Clara", "Toronto", "Toronto", "Urbana"]
countries: ["USA", "Canada", "Canada", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_Diffusion_Renderer_Neural_Inverse_and_Forward_Rendering_with_Video_Diffusion_CVPR_2025_paper.html

authors:
Ruofan Liang, Zan Gojcic, Huan Ling, Jacob Munkberg, Jon Hasselgren, Chih-Hao Lin, Jun Gao, Alexander Keller, Nandita Vijaykumar, Sanja Fidler, Zian Wang

abstract:
Understanding and modeling lighting effects are fundamental tasks in computer vision and graphics. Classic physically-based rendering (PBR) accurately simulates the light transport, but relies on precise scene representations--explicit 3D geometry, high-quality material properties, and lighting conditions--that are often impractical to obtain in real-world scenarios. Therefore, we introduce Diffusion Renderer, a neural approach that addresses the dual problem of inverse and forward rendering within a holistic framework. Leveraging powerful video diffusion model priors, the inverse rendering model accurately estimates G-buffers from real-world videos, providing an interface for image editing tasks, and training data for the rendering model. Conversely, our rendering model generates photorealistic images from G-buffers without explicit light transport simulation. Specifically, we first train a video diffusion model for inverse rendering on synthetic data, which generalizes well to real-world videos and allows us to auto-label diverse real-world videos. We then co-train our rendering model using both synthetic and auto-labeled real-world data. Experiments demonstrate that Diffusion Renderer effectively approximates inverse and forwards rendering, consistently outperforming the state-of-the-art. Our model enables practical applications from a single video input--including relighting, material editing, and realistic object insertion.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_Diffusion_Renderer_Neural_Inverse_and_Forward_Rendering_with_Video_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_Diffusion_Renderer_Neural_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Ruofan and Gojcic, Zan and Ling, Huan and Munkberg, Jacob and Hasselgren, Jon and Lin, Chih-Hao and Gao, Jun and Keller, Alexander and Vijaykumar, Nandita and Fidler, Sanja and Wang, Zian},
    title     = {Diffusion Renderer: Neural Inverse and Forward Rendering with Video Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26069-26080}
}

raw_affiliation:
[{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}, {"affiliation": "Vector Institute", "city": "Toronto", "country": "Canada"}, {"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana", "country": "USA"}]


title: Harnessing Frequency Spectrum Insights for Image Copyright Protection Against Diffusion Models
authors: Zhenguang Liu, Chao Shuai, Shaojing Fan, Ziping Dong, Jinwu Hu, Zhongjie Ba, Kui Ren
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18653-18662
cities: ["Hangzhou", "Hangzhou", "Singapore", "Guangzhou"]
countries: ["China", "China", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Harnessing_Frequency_Spectrum_Insights_for_Image_Copyright_Protection_Against_Diffusion_CVPR_2025_paper.html

abstract:
Diffusion models have achieved remarkable success in novel view synthesis, but their reliance on large, diverse, and often untraceable Web datasets has raised pressing concerns about image copyright protection. Current methods fall short in reliably identifying unauthorized image use, as they struggle to generalize across varied generation tasks and fail when the training dataset includes images from multiple sources with few identifiable (watermarked or poisoned) samples. In this paper, we present novel evidence that diffusion-generated images faithfully preserve the statistical properties of their training data, particularly reflected in their spectral features. Leveraging this insight, we introduce CoprGuard, a robust frequency domain watermarking framework to safeguard against unauthorized image usage in diffusion model training and fine-tuning. CoprGuard demonstrates remarkable effectiveness against a wide range of models, from naive diffusion models to sophisticated text-to-image models, and is robust even when watermarked images comprise a mere 1% of the training dataset. This robust and versatile approach empowers content owners to protect their intellectual property in the era of AI-driven image generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Harnessing_Frequency_Spectrum_Insights_for_Image_Copyright_Protection_Against_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Harnessing_Frequency_Spectrum_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Zhenguang and Shuai, Chao and Fan, Shaojing and Dong, Ziping and Hu, Jinwu and Ba, Zhongjie and Ren, Kui},
    title     = {Harnessing Frequency Spectrum Insights for Image Copyright Protection Against Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18653-18662}
}

raw_affiliation:
[{"affiliation": "The State Key Laboratory of Blockchain and Data Security, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security", "city": "Hangzhou", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}]

affiliations:
["The State Key Laboratory of Blockchain and Data Security, Zhejiang University", "Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security", "National University of Singapore", "South China University of Technology"]


title: Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels
authors: Qiming Xia, Wenkai Lin, Haoen Xiang, Xun Huang, Siheng Chen, Zhen Dong, Cheng Wang, Chenglu Wen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1418-1428
affiliations: ["Xiamen University", "Xiamen University", "Shanghai Jiao Tong University", "Wuhan University"]
cities: ["Xiamen", "Xiamen", "Shanghai", "Wuhan"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xia_Learning_to_Detect_Objects_from__Multi-Agent_LiDAR_Scans_without_CVPR_2025_paper.html

abstract:
Unsupervised 3D object detection serves as an important solution for offline 3D object annotation. However, due to the data sparsity and limited views, the clustering-based label fitting in unsupervised object detection often generates low-quality pseudo-labels. Multi-agent collaborative dataset, which involves the sharing of complementary observations among agents, holds the potential to break through this bottleneck. In this paper, we introduce a novel unsupervised method that learns to Detect Objects from Multi-Agent LiDAR scans, termed DOtA, without using labels from external. DOtA first uses the internally shared ego-pose and ego-shape of collaborative agents to initialize the detector, leveraging the generalization performance of neural networks to infer preliminary labels. Subsequently, DOtA uses the complementary observations between agents to perform multi-scale encoding on preliminary labels, then decodes high-quality and low-quality labels. These labels are further used as prompts to guide a correct feature learning process, thereby enhancing the performance of the unsupervised object detection task. Extensive experiments on the V2V4Real and OPV2V datasets show that our DOtA outperforms state-of-the-art unsupervised 3D object detection methods. Additionally, we also validate the effectiveness of the DOtA labels under various collaborative perception frameworks. The code is available at https://github.com/xmuqimingxia/DOtA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xia_Learning_to_Detect_Objects_from__Multi-Agent_LiDAR_Scans_without_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xia_Learning_to_Detect_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xia_2025_CVPR,
    author    = {Xia, Qiming and Lin, Wenkai and Xiang, Haoen and Huang, Xun and Chen, Siheng and Dong, Zhen and Wang, Cheng and Wen, Chenglu},
    title     = {Learning to Detect Objects from  Multi-Agent LiDAR Scans without Manual Labels},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1418-1428}
}

raw_affiliation:
[{"affiliation": "Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}]


title: DeepLA-Net: Very Deep Local Aggregation Networks for Point Cloud Analysis
authors: Ziyin Zeng, Mingyue Dong, Jian Zhou, Huan Qiu, Zhen Dong, Man Luo, Bijun Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1330-1341
affiliations: ["Wuhan University", "Dongfeng USharing Technology Co."]
cities: ["Wuhan", "Wuhan"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zeng_DeepLA-Net_Very_Deep_Local_Aggregation_Networks_for_Point_Cloud_Analysis_CVPR_2025_paper.html

abstract:
Due to the irregular and disordered data structure in 3D point clouds, prior works have focused on designing more sophisticated local representation methods to capture these complex local patterns. However, the recognition performance has saturated over the past few years, indicating that increasingly complex and redundant designs no longer make improvements to local learning. This phenomenon prompts us to diverge from the trend in 3D vision and instead pursue an alternative and successful solution: deeper neural networks. In this paper, we propose DeepLA-Net, a series of very deep networks for point cloud analysis. The key insight of our approach is to exploit a small but mighty local learning block, which reduces 10xfewer FLOPs, enabling the construction of very deep networks. Furthermore, we design a training supervision strategy to ensure smooth gradient backpropagation and optimization in very deep networks. We construct the DeepLA-Net family with a depth of up to 120 blocks --- at least 5xdeeper than recent methods --- trained on a single RTX 3090. An ensemble of the DeepLA-Net achieves state-of-the-art performance on classification and segmentation tasks of S3DIS Area5 (+2.2% mIoU), ScanNet test set (+1.6% mIoU), ScanObjectNN (+2.1% OA), and ShapeNetPart (+0.9% cls.mIoU).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zeng_DeepLA-Net_Very_Deep_Local_Aggregation_Networks_for_Point_Cloud_Analysis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zeng_DeepLA-Net_Very_Deep_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zeng_2025_CVPR,
    author    = {Zeng, Ziyin and Dong, Mingyue and Zhou, Jian and Qiu, Huan and Dong, Zhen and Luo, Man and Li, Bijun},
    title     = {DeepLA-Net: Very Deep Local Aggregation Networks for Point Cloud Analysis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1330-1341}
}

raw_affiliation:
[{"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Dongfeng USharing Technology Co.", "city": "Wuhan", "country": "China"}]


title: Multi-Layer Visual Feature Fusion in Multimodal LLMs: Methods, Analysis, and Best Practices
authors: Junyan Lin, Haoran Chen, Yue Fan, Yingqi Fan, Xin Jin, Hui Su, Jinlan Fu, Xiaoyu Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4156-4166
cities: ["Ningbo", "Qingdao", "Hangzhou", "Unknown", "Beijing", "Hangzhou", "Singapore"]
countries: ["China", "China", "China", "Unknown", "China", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_Multi-Layer_Visual_Feature_Fusion_in_Multimodal_LLMs_Methods_Analysis_and_CVPR_2025_paper.html

abstract:
Multimodal Large Language Models (MLLMs) have made significant advancements in recent years, with visual features playing an increasingly critical role in enhancing model performance. However, the integration of multi-layer visual features in MLLMs remains underexplored, particularly with regard to optimal layer selection and fusion strategies. Existing methods often rely on arbitrary design choices, leading to suboptimal outcomes. In this paper, we systematically investigate two core aspects of multi-layer visual feature fusion: (1) selecting the most effective visual layers and (2) identifying the best fusion approach with the language model. Our experiments reveal that while combining visual features from multiple stages improves generalization, incorporating additional features from the same stage typically leads to diminished performance. Furthermore, we find that direct fusion of multi-layer visual features at the input stage consistently yields superior and more stable performance across various configurations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_Multi-Layer_Visual_Feature_Fusion_in_Multimodal_LLMs_Methods_Analysis_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_Multi-Layer_Visual_Feature_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Junyan and Chen, Haoran and Fan, Yue and Fan, Yingqi and Jin, Xin and Su, Hui and Fu, Jinlan and Shen, Xiaoyu},
    title     = {Multi-Layer Visual Feature Fusion in Multimodal LLMs: Methods, Analysis, and Best Practices},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4156-4166}
}

raw_affiliation:
[{"affiliation": "Ningbo Key Laboratory of Spatial Intelligence and Digital Derivative, Institute of Digital Twin, EIT", "city": "Ningbo", "country": "China"}, {"affiliation": "Ocean University of China", "city": "Qingdao", "country": "China"}, {"affiliation": "Zhejiang Gongshang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Genmo.ai", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Meituan Inc.", "city": "Beijing", "country": "China"}, {"affiliation": "Engineering Research Center of Chiplet Design and Manufacturing of Zhejiang Province", "city": "Hangzhou", "country": "China"}, {"affiliation": "NUS", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Ningbo Key Laboratory of Spatial Intelligence and Digital Derivative, Institute of Digital Twin, EIT", "Ocean University of China", "Zhejiang Gongshang University", "Genmo.ai", "Meituan Inc.", "Engineering Research Center of Chiplet Design and Manufacturing of Zhejiang Province", "NUS"]


authors: Zhuguanyu Wu, Jiayi Zhang, Jiaxin Chen, Jinyang Guo, Di Huang, Yunhong Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9686-9695
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_APHQ-ViT_Post-Training_Quantization_with_Average_Perturbation_Hessian_Based_Reconstruction_for_CVPR_2025_paper.html

title:
APHQ-ViT: Post-Training Quantization with Average Perturbation Hessian Based Reconstruction for Vision Transformers

abstract:
Vision Transformers (ViTs) have become one of the most commonly used backbones for vision tasks. Despite their remarkable performance, they often suffer significant accuracy drop when quantized for practical deployment, particularly by post-training quantization (PTQ) under ultra-low bits. Recently, reconstruction-based PTQ methods have shown promising performance in quantizing Convolutional Neural Networks (CNNs). However, they fail when applied to ViTs, primarily due to the inaccurate estimation of output importance and the substantial accuracy degradation in quantizing post-GELU activations. To address these issues, we propose APHQ-ViT, a novel PTQ approach based on importance estimation with Average Perturbation Hessian (APH). Specifically, we first thoroughly analyze the current approximation approaches with Hessian loss, and propose an improved average perturbation Hessian loss. To deal with the quantization of the post-GELU activations, we design an MLP-Reconstruction (MR) method by replacing the GELU function in MLP with ReLU and reconstructing it by the APH loss on a small unlabeled calibration set. Extensive experiments demonstrate that APHQ-ViT using linear quantizers outperforms existing PTQ methods by substantial margins in 3-bit and 4-bit across different vision tasks. The source code is available at https://github.com/GoatWu/APHQ-ViT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_APHQ-ViT_Post-Training_Quantization_with_Average_Perturbation_Hessian_Based_Reconstruction_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_APHQ-ViT_Post-Training_Quantization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Zhuguanyu and Zhang, Jiayi and Chen, Jiaxin and Guo, Jinyang and Huang, Di and Wang, Yunhong},
    title     = {APHQ-ViT: Post-Training Quantization with Average Perturbation Hessian Based Reconstruction for Vision Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9686-9695}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, China", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science and Engineering, Beihang University, Beijing, China", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Beihang University, Beijing, China", "city": "Beijing", "country": "China"}]

affiliations:
["State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, China", "School of Computer Science and Engineering, Beihang University, Beijing, China", "School of Artificial Intelligence, Beihang University, Beijing, China"]


title: AdaptCMVC: Robust Adaption to Incremental Views in Continual Multi-view Clustering
authors: Jing Wang, Songhe Feng, Kristoffer Knutsen Wickstrøm, Michael C. Kampffmeyer
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10285-10294
cities: ["Beijing", "Troms\u00f8"]
countries: ["China", "Norway"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_AdaptCMVC_Robust_Adaption_to_Incremental_Views_in_Continual_Multi-view_Clustering_CVPR_2025_paper.html

abstract:
Most Multi-view Clustering approaches assume that all views are available for clustering. However, this assumption is often unrealistic as views are incrementally accumulated over time, leading to a need for continual multi-view clustering (CMVC) methods. Current approaches to CMVC leverage late fusion-based approaches, where a new model is typically learned individually for each view to obtain the corresponding partition matrix, and then used to update a consensus matrix via a moving average. These approaches are prone to view-specific noise and struggle to adapt to large gaps between different views. To address these shortcomings, we reconsider CMVC from the perspective of domain adaptation and propose AdaptCMVC, which learns how to incrementally accumulate knowledge of new views as they become available and prevents catastrophic forgetting. Specifically, a self-training framework is introduced to extend the model to new views, particularly designed to be robust to view-specific noise. Further, to combat catastrophic forgetting, a structure alignment mechanism is proposed to enable the model to explore the global group structure across multiple views. Experiments on several multi-view benchmarks demonstrate the effectiveness of our proposed method on the CMVC task. The code is available at: AdaptCMVC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_AdaptCMVC_Robust_Adaption_to_Incremental_Views_in_Continual_Multi-view_Clustering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_AdaptCMVC_Robust_Adaption_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Jing and Feng, Songhe and Wickstr{\o}m, Kristoffer Knutsen and Kampffmeyer, Michael C.},
    title     = {AdaptCMVC: Robust Adaption to Incremental Views in Continual Multi-view Clustering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10285-10294}
}

raw_affiliation:
[{"affiliation": "School of Computer Science & Technology, Beijing Jiaotong University", "city": "Beijing", "country": "China"}, {"affiliation": "Department of Physics and Technology, UiT The Arctic University of Norway", "city": "Troms\u00f8", "country": "Norway"}]

affiliations:
["School of Computer Science & Technology, Beijing Jiaotong University", "Department of Physics and Technology, UiT The Arctic University of Norway"]


title: Omni-Scene: Omni-Gaussian Representation for Ego-Centric Sparse-View Scene Reconstruction
authors: Dongxu Wei, Zhiqi Li, Peidong Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22317-22327
cities: ["Hangzhou", "Hangzhou", "Hangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wei_Omni-Scene_Omni-Gaussian_Representation_for_Ego-Centric_Sparse-View_Scene_Reconstruction_CVPR_2025_paper.html

abstract:
Prior works employing pixel-based Gaussian representation have demonstrated efficacy in feed-forward sparse-view reconstruction. However, such representation necessitates cross-view overlap for accurate depth estimation, and is challenged by object occlusions and frustum truncations. As a result, these methods require scene-centric data acquisition to maintain cross-view overlap and complete scene visibility to circumvent occlusions and truncations, which limits their applicability to scene-centric reconstruction. In contrast, in autonomous driving scenarios, a more practical paradigm is ego-centric reconstruction, which is characterized by minimal cross-view overlap and frequent occlusions and truncations. The limitations of pixel-based representation thus hinder the utility of prior works in this task. In light of this, this paper conducts an in-depth analysis of different representations, and introduces Omni-Gaussian representation with tailored network design to complement their strengths and mitigate their drawbacks. Experiments show that our method significantly surpasses state-of-the-art methods, pixelSplat and MVSplat, in ego-centric reconstruction, and achieves comparable performance to prior works in scene-centric reconstruction. Our code is available at https://github.com/WU-CVGL/Omni-Scene.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wei_Omni-Scene_Omni-Gaussian_Representation_for_Ego-Centric_Sparse-View_Scene_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wei_Omni-Scene_Omni-Gaussian_Representation_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wei_2025_CVPR,
    author    = {Wei, Dongxu and Li, Zhiqi and Liu, Peidong},
    title     = {Omni-Scene: Omni-Gaussian Representation for Ego-Centric Sparse-View Scene Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22317-22327}
}

raw_affiliation:
[{"affiliation": "School of Engineering, Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "College of Computer Science and Technology, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Institute of Advanced Technology, Westlake Institute for Advanced Study", "city": "Hangzhou", "country": "China"}]

affiliations:
["School of Engineering, Westlake University", "College of Computer Science and Technology, Zhejiang University", "Institute of Advanced Technology, Westlake Institute for Advanced Study"]


title: 3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26576-26586
cities: ["Singapore", "Beijing", "Shanghai", "Hong Kong"]
countries: ["Singapore", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_3DTopia-XL_Scaling_High-quality_3D_Asset_Generation_via_Primitive_Diffusion_CVPR_2025_paper.html

authors:
Zhaoxi Chen, Jiaxiang Tang, Yuhao Dong, Ziang Cao, Fangzhou Hong, Yushi Lan, Tengfei Wang, Haozhe Xie, Tong Wu, Shunsuke Saito, Liang Pan, Dahua Lin, Ziwei Liu

abstract:
The increasing demand for high-quality 3D assets across various industries necessitates efficient and automated 3D content creation. Despite recent advancements in 3D generative models, existing methods still face challenges with optimization speed, geometric fidelity, and the lack of assets for physically based rendering (PBR). In this paper, we introduce 3DTopia-XL, a scalable native 3D generative model designed to overcome these limitations. 3DTopia-XL leverages a novel primitive-based 3D representation, PrimX, which encodes detailed shape, albedo, and material field into a compact tensorial format, facilitating the modeling of high-resolution geometry with PBR assets. On top of the novel representation, we propose a generative framework based on Diffusion Transformer (DiT), which comprises 1) Primitive Patch Compression, 2) and Latent Primitive Diffusion. 3DTopia-XL learns to generate high-quality 3D assets from textual or visual inputs. Extensive qualitative and quantitative experiments are conducted to demonstrate that 3DTopia-XL significantly outperforms existing methods in generating high-quality 3D assets with fine-grained textures and materials, efficiently bridging the quality gap between generative models and real-world applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_3DTopia-XL_Scaling_High-quality_3D_Asset_Generation_via_Primitive_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_3DTopia-XL_Scaling_High-quality_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Zhaoxi and Tang, Jiaxiang and Dong, Yuhao and Cao, Ziang and Hong, Fangzhou and Lan, Yushi and Wang, Tengfei and Xie, Haozhe and Wu, Tong and Saito, Shunsuke and Pan, Liang and Lin, Dahua and Liu, Ziwei},
    title     = {3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26576-26586}
}

raw_affiliation:
[{"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["S-Lab, Nanyang Technological University", "Peking University", "Shanghai AI Laboratory", "The Chinese University of Hong Kong"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1180-1189
affiliations: ["Carnegie Mellon University", "Stony Brook University", "National Tsing Hua University", "Amazon"]
cities: ["Pittsburgh", "Stony Brook", "Hsinchu", "Seattle"]
countries: ["USA", "USA", "Taiwan", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_UA-Pose_Uncertainty-Aware_6D_Object_Pose_Estimation_and_Online_Object_Completion_CVPR_2025_paper.html

title:
UA-Pose: Uncertainty-Aware 6D Object Pose Estimation and Online Object Completion with Partial References

authors:
Ming-Feng Li, Xin Yang, Fu-En Wang, Hritam Basak, Yuyin Sun, Shreekant Gayaka, Min Sun, Cheng-Hao Kuo

abstract:
6D object pose estimation has shown strong generalizability to novel objects. However, existing methods often require either a complete, well-reconstructed 3D model or numerous reference images that fully cover the object. Estimating 6D poses from partial references, which capture only fragments of an object's appearance and geometry, remains challenging. To address this, we propose UA-Pose, an uncertainty-aware approach for 6D object pose estimation and online object completion specifically designed for partial references. We assume access to either (1) a limited set of RGBD images with known poses or (2) a single 2D image. For the first case, we initialize a partial object 3D model based on the provided images and poses, while for the second, we use image-to-3D techniques to generate an initial object 3D model. Our method integrates uncertainty into the incomplete 3D model, distinguishing between seen and unseen regions. This uncertainty enables confidence assessment in pose estimation and guides an uncertainty-aware sampling strategy for online object completion, enhancing robustness in pose estimation accuracy and improving object completeness. We evaluate our method on the YCB-Video, YCBInEOAT, and HO3D datasets, including RGBD sequences of YCB objects manipulated by robots and human hands. Experimental results demonstrate significant performance improvements over existing methods, particularly when object observations are incomplete or partially captured.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_UA-Pose_Uncertainty-Aware_6D_Object_Pose_Estimation_and_Online_Object_Completion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_UA-Pose_Uncertainty-Aware_6D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Ming-Feng and Yang, Xin and Wang, Fu-En and Basak, Hritam and Sun, Yuyin and Gayaka, Shreekant and Sun, Min and Kuo, Cheng-Hao},
    title     = {UA-Pose: Uncertainty-Aware 6D Object Pose Estimation and Online Object Completion with Partial References},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1180-1189}
}

raw_affiliation:
[{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "Stony Brook University", "city": "Stony Brook", "country": "USA"}, {"affiliation": "National Tsing Hua University", "city": "Hsinchu", "country": "Taiwan"}, {"affiliation": "Amazon", "city": "Seattle", "country": "USA"}]


authors: Yuanmin Tang, Jing Yu, Keke Gai, Jiamin Zhuang, Gang Xiong, Gaopeng Gou, Qi Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24785-24795
cities: ["Beijing", "Beijing", "Beijing", "Beijing", "Adelaide"]
countries: ["China", "China", "China", "China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_Missing_Target-Relevant_Information_Prediction_with_World_Model_for_Accurate_Zero-Shot_CVPR_2025_paper.html

title:
Missing Target-Relevant Information Prediction with World Model for Accurate Zero-Shot Composed Image Retrieval

abstract:
Zero-Shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with a broad range of visual content manipulation intent across domain, scene, object, and attribute. The key challenge for ZS-CIR tasks is to modify a reference image according to manipulation text to accurately retrieve a target image, especially when the reference image is missing essential target content. In this paper, we propose a novel prediction-based mapping network, named PrediCIR, to adaptively predict the missing target visual content in reference images in the latent space before mapping for accurate ZS-CIR. Specifically, a world view generation module first constructs a source view by omitting certain visual content of a target view, coupled with an action that includes the manipulation intent derived from existing image-caption pairs. Then, a target content prediction module trains a world model as a predictor to adaptively predict the missing visual information guided by user intention in manipulating text at the latent space. The two modules map an image with the predicted relevant information to a pseudo-word token without extra supervision. Our model shows strong generalization ability on six ZS-CIR tasks. It obtains consistent and significant performance boosts ranging from 1.73% to 4.45% over the best methods and achieves new state-of-the-art results on ZS-CIR. Our code is available at https://github.com/Pter61/predicir.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_Missing_Target-Relevant_Information_Prediction_with_World_Model_for_Accurate_Zero-Shot_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_Missing_Target-Relevant_Information_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Yuanmin and Yu, Jing and Gai, Keke and Zhuang, Jiamin and Xiong, Gang and Gou, Gaopeng and Wu, Qi},
    title     = {Missing Target-Relevant Information Prediction with World Model for Accurate Zero-Shot Composed Image Retrieval},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24785-24795}
}

raw_affiliation:
[{"affiliation": "Institute of Information Engineering, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Cyber Security , University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Information Engineering, Minzu University of China", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Institute of T echnology", "city": "Beijing", "country": "China"}, {"affiliation": "University of Adelaide", "city": "Adelaide", "country": "Australia"}]

affiliations:
["Institute of Information Engineering, Chinese Academy of Sciences", "School of Cyber Security , University of Chinese Academy of Sciences", "School of Information Engineering, Minzu University of China", "Beijing Institute of T echnology", "University of Adelaide"]


title: Binarized Mamba-Transformer for Lightweight Quad Bayer HybridEVS Demosaicing
authors: Shiyang Zhou, Haijin Zeng, Yunfan Lu, Tong Shao, Ke Tang, Yongyong Chen, Jie Liu, Jingyong Su
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8817-8827
cities: ["Shenzhen", "Cambridge", "Guangzhou"]
countries: ["China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Binarized_Mamba-Transformer_for_Lightweight_Quad_Bayer_HybridEVS_Demosaicing_CVPR_2025_paper.html

abstract:
Quad Bayer demosaicing is the central challenge for enabling the widespread application of Hybrid Event-based Vision Sensors (HybridEVS). Although existing learning-based methods that leverage long-range dependency modeling have achieved promising results, their complexity severely limits deployment on mobile devices for real-world applications. To address these limitations, we propose a lightweight Mamba-based binary neural network designed for efficient and high-performing demosaicing of HybridEVS RAW images. First, to effectively capture both global and local dependencies, we introduce a hybrid Binarized Mamba-Transformer architecture that combines the strengths of the Mamba and Swin Transformer architectures. Next, to significantly reduce computational complexity, we propose a binarized Mamba (Bi-Mamba), which binarizes all projections while retaining the core Selective Scan in full precision. Bi-Mamba also incorporates additional global visual information to enhance global context and mitigate precision loss. We conduct quantitative and qualitative experiments to demonstrate the effectiveness of BMTNet in both performance and computational efficiency, providing a lightweight demosaicing solution suited for real-world edge devices. Our codes and models are available at https://github.com/Clausy9/BMTNet.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Binarized_Mamba-Transformer_for_Lightweight_Quad_Bayer_HybridEVS_Demosaicing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Binarized_Mamba-Transformer_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Shiyang and Zeng, Haijin and Lu, Yunfan and Shao, Tong and Tang, Ke and Chen, Yongyong and Liu, Jie and Su, Jingyong},
    title     = {Binarized Mamba-Transformer for Lightweight Quad Bayer HybridEVS Demosaicing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8817-8827}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of Technology (Shenzhen)", "city": "Shenzhen", "country": "China"}, {"affiliation": "Harvard University", "city": "Cambridge", "country": "USA"}, {"affiliation": "Hong Kong University of Science and Technology, Guangzhou", "city": "Guangzhou", "country": "China"}]

affiliations:
["Harbin Institute of Technology (Shenzhen)", "Harvard University", "Hong Kong University of Science and Technology, Guangzhou"]


title: DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation
authors: Jianzong Wu, Chao Tang, Jingbo Wang, Yanhong Zeng, Xiangtai Li, Yunhai Tong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28684-28693
cities: ["Beijing", "Shanghai", "Singapore", "Beijing"]
countries: ["China", "China", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_DiffSensei_Bridging_Multi-Modal_LLMs_and_Diffusion_Models_for_Customized_Manga_CVPR_2025_paper.html

abstract:
Story visualization, the task of creating visual narratives from textual descriptions, has seen progress with text-to-image generation models. However, these models often lack effective control over character appearances and interactions, particularly in multi-character scenes. To address these limitations, we propose a new task: customized manga generation and introduce DiffSensei, an innovative framework specifically designed for generating manga with dynamic multi-character control. DiffSensei integrates a diffusion-based image generator with a multimodal large language model (MLLM) that acts as a text-compatible identity adapter. Our approach employs masked cross-attention to seamlessly incorporate character features, enabling precise layout control without direct pixel transfer. Additionally, the MLLM-based adapter adjusts character features to align with panel-specific text cues, allowing flexible adjustments in character expressions, poses, and actions. We also introduce MangaZero, a large-scale dataset tailored to this task, containing 43,264 manga pages and 427,147 annotated panels, supporting the visualization of varied character interactions and movements across sequential frames. Extensive experiments demonstrate that DiffSensei outperforms existing models, marking a significant advancement in manga generation by enabling text-adaptable character customization. The code, model, and dataset will be open-sourced to the community.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_DiffSensei_Bridging_Multi-Modal_LLMs_and_Diffusion_Models_for_Customized_Manga_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Jianzong and Tang, Chao and Wang, Jingbo and Zeng, Yanhong and Li, Xiangtai and Tong, Yunhai},
    title     = {DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28684-28693}
}

raw_affiliation:
[{"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Bytedance Seed Project", "city": "Beijing", "country": "China"}]

affiliations:
["Peking University", "Shanghai AI Laboratory", "Nanyang Technological University", "Bytedance Seed Project"]


authors: Chan Hur, Jeong-hun Hong, Dong-hun Lee, Dabin Kang, Semin Myeong, Sang-hyo Park, Hyeyoung Park
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24077-24086
affiliations: ["School of Computer Science and Engineering, Kyungpook National University"]
cities: ["Daegu"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hur_Narrating_the_Video_Boosting_Text-Video_Retrieval_via_Comprehensive_Utilization_of_CVPR_2025_paper.html

title:
Narrating the Video: Boosting Text-Video Retrieval via Comprehensive Utilization of Frame-Level Captions

abstract:
In recent text-video retrieval, the use of additional captions from vision-language models has shown promising effects on the performance. However, existing models using additional captions often have struggled to capture the rich semantics, including temporal changes, inherent in the video. In addition, incorrect information caused by generative models can lead to inaccurate retrieval. To address these issues, we propose a new framework, Narrating the Video (NarVid), which strategically leverages the comprehensive information available from frame-level captions, the narration. The proposed NarVid exploits narration in multiple ways: 1) feature enhancement through cross-modal interactions between narration and video, 2) query-aware adaptive filtering to suppress irrelevant or incorrect information, 3) dual-modal matching score by adding query-video similarity and query-narration similarity, and 4) hard-negative loss to learn discriminative features from multiple perspectives using the two similarities from different views. Experimental results demonstrate that NarVid achieves state-of-the-art performance on various benchmark datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hur_Narrating_the_Video_Boosting_Text-Video_Retrieval_via_Comprehensive_Utilization_of_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hur_Narrating_the_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hur_2025_CVPR,
    author    = {Hur, Chan and Hong, Jeong-hun and Lee, Dong-hun and Kang, Dabin and Myeong, Semin and Park, Sang-hyo and Park, Hyeyoung},
    title     = {Narrating the Video: Boosting Text-Video Retrieval via Comprehensive Utilization of Frame-Level Captions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24077-24086}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Kyungpook National University", "city": "Daegu", "country": "South Korea"}]


title: IDEA-Bench: How Far are Generative Models from Professional Designing?
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18541-18551
cities: ["Beijing", "Beijing", "Hangzhou", "Beijing", "Hangzhou"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_IDEA-Bench_How_Far_are_Generative_Models_from_Professional_Designing_CVPR_2025_paper.html

authors:
Chen Liang, Lianghua Huang, Jingwu Fang, Huanzhang Dou, Wei Wang, Zhi-Fan Wu, Yupeng Shi, Junge Zhang, Xin Zhao, Yu Liu

abstract:
Recent advancements in image generation models enable the creation of high-quality images and targeted modifications based on textual instructions. Some models even support multimodal complex guidance and demonstrate robust task generalization capabilities. However, they still fall short of meeting the nuanced, professional demands of designers. To bridge this gap, we introduce IDEA-Bench, a comprehensive benchmark designed to advance image generation models toward applications with robust task generalization. IDEA-Bench comprises 100 professional image generation tasks and 275 specific cases, categorized into five major types based on the current capabilities of existing models. Furthermore, we provide a representative subset of 18 tasks with enhanced evaluation criteria to facilitate more nuanced and reliable evaluations using Multimodal Large Language Models (MLLMs). By assessing models' ability to comprehend and execute novel, complex tasks, IDEA-Bench paves the way toward the development of generative models with autonomous and versatile visual generation capabilities.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_IDEA-Bench_How_Far_are_Generative_Models_from_Professional_Designing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_IDEA-Bench_How_Far_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Chen and Huang, Lianghua and Fang, Jingwu and Dou, Huanzhang and Wang, Wei and Wu, Zhi-Fan and Shi, Yupeng and Zhang, Junge and Zhao, Xin and Liu, Yu},
    title     = {IDEA-Bench: How Far are Generative Models from Professional Designing?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18541-18551}
}

raw_affiliation:
[{"affiliation": "Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Ariti\ufb01cial Intelligence, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Tongyi Lab, Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of Science and Technology Beijing", "city": "Beijing", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]

affiliations:
["Institute of Automation, Chinese Academy of Sciences", "School of Ariti\ufb01cial Intelligence, University of Chinese Academy of Sciences", "Tongyi Lab, Alibaba Group", "University of Science and Technology Beijing", "Zhejiang University"]


title: Interpretable Image Classification via Non-parametric Part Prototype Learning
authors: Zhijie Zhu, Lei Fan, Maurice Pagnucco, Yang Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9762-9771
raw_affiliation: [{"affiliation": "University of New South Wales", "city": "Sydney", "country": "Australia"}]
affiliations: ["University of New South Wales"]
cities: ["Sydney"]
countries: ["Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Interpretable_Image_Classification_via_Non-parametric_Part_Prototype_Learning_CVPR_2025_paper.html

abstract:
Classifying images with an interpretable decision-making process is a long-standing problem in computer vision. In recent years, Prototypical Part Networks has gained traction as an approach for self-explainable neural networks, due to their ability to mimic human visual reasoning by providing explanations based on prototypical object parts. However, the quality of the explanations generated by these methods leaves room for improvement, as the prototypes usually focus on repetitive and redundant concepts. Leveraging recent advances in prototype learning, we present a framework for part-based interpretable image classification that learns a set of semantically distinctive object parts for each class, and provides diverse and comprehensive explanations. The core of our method is to learn the part-prototypes in a non-parametric fashion, through clustering deep features extracted from foundation vision models that encode robust semantic information. To quantitatively evaluate the quality of explanations provided by ProtoPNets, we introduce Distinctiveness Score and Comprehensiveness Score. Through evaluation on CUB-200-2011, Stanford Cars and Stanford Dogs datasets, we show that our framework compares favourably against existing ProtoPNets while achieving better interpretability.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Interpretable_Image_Classification_via_Non-parametric_Part_Prototype_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Interpretable_Image_Classification_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Zhijie and Fan, Lei and Pagnucco, Maurice and Song, Yang},
    title     = {Interpretable Image Classification via Non-parametric Part Prototype Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9762-9771}
}


title: PhD: A ChatGPT-Prompted Visual Hallucination Evaluation Dataset
authors: Jiazhen Liu, Yuhan Fu, Ruobing Xie, Runquan Xie, Xingwu Sun, Fengzong Lian, Zhanhui Kang, Xirong Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19857-19866
affiliations: ["Renmin University of China", "Machine Learning Platform Department, Tencent"]
cities: ["Beijing", "Shenzhen"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_PhD_A_ChatGPT-Prompted_Visual_Hallucination_Evaluation_Dataset_CVPR_2025_paper.html

abstract:
Multimodal Large Language Models (MLLMs) hallucinate, resulting in an emerging topic of visual hallucination evaluation (VHE). This paper contributes a ChatGPT-Prompted visual hallucination evaluation Dataset (PhD) for objective VHE at a large scale. The essence of VHE is to ask an MLLM questions about specific images to assess its susceptibility to hallucination. Depending on what to ask (objects, attributes, sentiment, etc.) and how the questions are asked, we structure PhD along two dimensions, i.e. task and mode. Five visual recognition tasks, ranging from low-level (object / attribute recognition) to middle-level (sentiment / position recognition and counting), are considered. Besides a normal visual QA mode, which we term PhD-base, PhD also asks questions with specious context (PhD-sec) or with incorrect context (PhD-icc), or with AI-generated counter common sense images (PhD-ccs). We construct PhD by a ChatGPT-assisted semi-automated pipeline, encompassing four pivotal modules: task-specific hallucinatory item (hitem) selection, hitem-embedded question generation, specious / incorrect context generation, and counter-common-sense (CCS) image generation. With over 14k daily images, 750 CCS images and 102k VQA triplets in total, PhD reveals considerable variability in MLLMs' performance across various modes and tasks, offering valuable insights into the nature of hallucination. As such, PhD stands as a potent tool not only for VHE but may also play a significant role in the refinement of MLLMs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_PhD_A_ChatGPT-Prompted_Visual_Hallucination_Evaluation_Dataset_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_PhD_A_ChatGPT-Prompted_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Jiazhen and Fu, Yuhan and Xie, Ruobing and Xie, Runquan and Sun, Xingwu and Lian, Fengzong and Kang, Zhanhui and Li, Xirong},
    title     = {PhD: A ChatGPT-Prompted Visual Hallucination Evaluation Dataset},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19857-19866}
}

raw_affiliation:
[{"affiliation": "Renmin University of China", "city": "Beijing", "country": "China"}, {"affiliation": "Machine Learning Platform Department, Tencent", "city": "Shenzhen", "country": "China"}]


title: CARL: A Framework for Equivariant Image Registration
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26014-26023
cities: ["Chapel Hill", "Paris", "Paris", "Salzburg", "Boston"]
countries: ["USA", "France", "France", "Austria", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Greer_CARL_A_Framework_for_Equivariant_Image_Registration_CVPR_2025_paper.html

authors:
Hastings Greer, Lin Tian, François-Xavier Vialard, Roland Kwitt, Raul San Jose Estepar, Marc Niethammer

abstract:
Image registration estimates spatial correspondences between image pairs. These estimates are typically obtained via numerical optimization or regression by a deep network. A desirable property is that a correspondence estimate (e.g., the true oracle correspondence) for an image pair is maintained under deformations of the input images. Formally, the estimator should be equivariant to a desired class of image transformations. In this work, we present careful analyses of equivariance properties in the context of multi-step deep registration networks. Based on these analyses we 1) introduce the notions of [U,U] equivariance (network equivariance to the same deformations of the input images) and [W,U] equivariance (where input images can undergo different deformations); we 2) show that in a suitable multi-step registration setup it is sufficient for overall [W,U] equivariance if the first step has [W,U] equivariance and all others have [U,U] equivariance; we 3) show that common displacement-predicting networks only exhibit [U,U] equivariance to translations instead of the more powerful [W,U] equivariance; and we 4) show how to achieve multi-step [W,U] equivariance via a coordinate-attention mechanism combined with displacement-predicting networks. Our approach obtains excellent practical performance for 3D abdomen, lung, and brain medical image registration. We match or outperform state-of-the-art (SOTA) registration approaches on all the datasets with a particularly strong performance for the challening abdomen registration.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Greer_CARL_A_Framework_for_Equivariant_Image_Registration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Greer_CARL_A_Framework_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Greer_2025_CVPR,
    author    = {Greer, Hastings and Tian, Lin and Vialard, Fran\c{c}ois-Xavier and Kwitt, Roland and Estepar, Raul San Jose and Niethammer, Marc},
    title     = {CARL: A Framework for Equivariant Image Registration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26014-26023}
}

raw_affiliation:
[{"affiliation": "UNC Chapel Hill", "city": "Chapel Hill", "country": "USA"}, {"affiliation": "LIGM, Universit\u00b4e Gustave Eiffel", "city": "Paris", "country": "France"}, {"affiliation": "MOKAPLAN, INRIA Paris", "city": "Paris", "country": "France"}, {"affiliation": "University of Salzburg", "city": "Salzburg", "country": "Austria"}, {"affiliation": "Harvard Medical School", "city": "Boston", "country": "USA"}]

affiliations:
["UNC Chapel Hill", "LIGM, Universit\u00b4e Gustave Eiffel", "MOKAPLAN, INRIA Paris", "University of Salzburg", "Harvard Medical School"]


title: ClimbingCap: Multi-Modal Dataset and Method for Rock Climbing in World Coordinate
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12312-12323
cities: ["Xiamen", "Xiamen", "Xiamen", "Beijing", "Ningbo", "Shanghai", "Z\u00fcrich"]
countries: ["China", "China", "China", "China", "China", "China", "Switzerland"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yan_ClimbingCap_Multi-Modal_Dataset_and_Method_for_Rock_Climbing_in_World_CVPR_2025_paper.html

authors:
Ming Yan, Xincheng Lin, Yuhua Luo, Shuqi Fan, Yudi Dai, Qixin Zhong, Lincai Zhong, Yuexin Ma, Lan Xu, Chenglu Wen, Siqi Shen, Cheng Wang

abstract:
Human Motion Recovery (HMR) research mainly focuses on ground-based motions such as running. The study on capturing climbing motion, an off-ground motion, is sparse. This is partly due to the limited availability of climbing motion datasets, especially large-scale and challenging 3D labeled datasets. To address the insufficiency of climbing motion datasets, we collect AscendMotion, a large-scale well-annotated, and challenging climbing motion dataset. It consists of 412k RGB, LiDAR frames, and IMU measurements, which includes the challenging climbing motions of 22 professional climbing coaches across 12 different rocks. Capturing the climbing motions is challenging as it requires precise recovery of not only the complex pose but also the global position of climbers. Although multiple global HMR methods have been proposed, they cannot faithfully capture climbing motions. To address the limitations of HMR methods for climbing, we propose ClimbingCap, a motion recovery method that reconstructs continuous 3D human climbing motion in a global coordinate system. One key insight is to use the RGB and the LiDAR modalities to separately reconstruct motions in camera coordinates and global coordinates and optimize them jointly. We demonstrate the quality of the AscendMotion dataset and present promising results from ClimbingCap. The AscendMotion dataset and the source code of ClimbingCap will be released publicly to the research community.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_ClimbingCap_Multi-Modal_Dataset_and_Method_for_Rock_Climbing_in_World_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_ClimbingCap_Multi-Modal_Dataset_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yan_2025_CVPR,
    author    = {Yan, Ming and Lin, Xincheng and Luo, Yuhua and Fan, Shuqi and Dai, Yudi and Zhong, Qixin and Zhong, Lincai and Ma, Yuexin and Xu, Lan and Wen, Chenglu and Shen, Siqi and Wang, Cheng},
    title     = {ClimbingCap: Multi-Modal Dataset and Method for Rock Climbing in World Coordinate},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12312-12323}
}

raw_affiliation:
[{"affiliation": "Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "National Institute for Data Science in Health and Medicine, Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, School of Informatics, Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "China National Climbing Team", "city": "Beijing", "country": "China"}, {"affiliation": "Ningbo Sports Work Training Team", "city": "Ningbo", "country": "China"}, {"affiliation": "ShanghaiTech University", "city": "Shanghai", "country": "China"}, {"affiliation": "ETH AI Center, ETH Z\u00a8urich", "city": "Z\u00fcrich", "country": "Switzerland"}]

affiliations:
["Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University", "National Institute for Data Science in Health and Medicine, Xiamen University", "Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, School of Informatics, Xiamen University", "China National Climbing Team", "Ningbo Sports Work Training Team", "ShanghaiTech University", "ETH AI Center, ETH Z\u00a8urich"]


title: DAGSM: Disentangled Avatar Generation with GS-enhanced Mesh
authors: Jingyu Zhuang, Di Kang, Linchao Bao, Liang Lin, Guanbin Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 292-303
cities: ["Guangzhou", "Shenzhen", "Shenzhen", "Guangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhuang_DAGSM_Disentangled_Avatar_Generation_with_GS-enhanced_Mesh_CVPR_2025_paper.html

abstract:
Text-driven avatar generation has gained significant attention owing to its convenience. However, existing methods typically model the human body with all garments as a single 3D model, limiting its usability, such as clothing replacement, and reducing user control over the generation process. To overcome the limitations above, we propose DAGSM, a novel pipeline that generates disentangled human bodies and garments from the given text prompts. Specifically, we model each part (e.g., body, upper/lower clothes) of the clothed human as one GS-enhanced mesh (GSM), which is a traditional mesh attached with 2D Gaussians to better handle complicated textures (e.g., woolen, translucent clothes) and produce realistic cloth animations. During the generation, we first create the unclothed body, followed by a sequence of individual cloth generation based on the body, where we introduce a semantic-based algorithm to achieve better human-cloth and garment-garment separation. To improve texture quality, we propose a view-consistent texture refinement module, including a cross-view attention mechanism for texture style consistency and an incident-angle-weighted denoising (IAW-DE) strategy to update the appearance. Extensive experiments have demonstrated that DAGSM generates high-quality disentangled avatars, supports clothing replacement and realistic animation, and outperforms the baselines in visual quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhuang_DAGSM_Disentangled_Avatar_Generation_with_GS-enhanced_Mesh_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhuang_DAGSM_Disentangled_Avatar_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhuang_2025_CVPR,
    author    = {Zhuang, Jingyu and Kang, Di and Bao, Linchao and Lin, Liang and Li, Guanbin},
    title     = {DAGSM: Disentangled Avatar Generation with GS-enhanced Mesh},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {292-303}
}

raw_affiliation:
[{"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Guangdong Key Laboratory of Big Data Analysis and Processing", "city": "Guangzhou", "country": "China"}]

affiliations:
["Sun Yat-sen University", "Tencent", "Peng Cheng Laboratory", "Guangdong Key Laboratory of Big Data Analysis and Processing"]


title: Estimating Body and Hand Motion in an Ego-sensed World
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7072-7084
affiliations: ["UC Berkeley", "ShanghaiTech", "UT Austin"]
cities: ["Berkeley", "Shanghai", "Austin"]
countries: ["USA", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yi_Estimating_Body_and_Hand_Motion_in_an_Ego-sensed_World_CVPR_2025_paper.html

authors:
Brent Yi, Vickie Ye, Maya Zheng, Yunqi Li, Lea Müller, Georgios Pavlakos, Yi Ma, Jitendra Malik, Angjoo Kanazawa

abstract:
We present EgoAllo, a system for human motion estimation from a head-mounted device. Using only egocentric SLAM poses and images, EgoAllo guides sampling from a conditional diffusion model to estimate 3D body pose, height, and hand parameters that capture a device wearer's actions in the allocentric coordinate frame of the scene. To achieve this, our key insight is in representation: we propose spatial and temporal invariance criteria for improving model performance, from which we derive a head motion conditioning parameterization that improves estimation by up to 18%. We also show how the bodies estimated by our system can improve hand estimation: the resulting kinematic and temporal constraints can reduce world-frame errors in single-frame estimates by 40%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yi_Estimating_Body_and_Hand_Motion_in_an_Ego-sensed_World_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yi_Estimating_Body_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yi_2025_CVPR,
    author    = {Yi, Brent and Ye, Vickie and Zheng, Maya and Li, Yunqi and M\"uller, Lea and Pavlakos, Georgios and Ma, Yi and Malik, Jitendra and Kanazawa, Angjoo},
    title     = {Estimating Body and Hand Motion in an Ego-sensed World},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7072-7084}
}

raw_affiliation:
[{"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "ShanghaiTech", "city": "Shanghai", "country": "China"}, {"affiliation": "UT Austin", "city": "Austin", "country": "USA"}]


title: A Bias-Free Training Paradigm for More General AI-generated Image Detection
authors: Fabrizio Guillaro, Giada Zingarini, Ben Usman, Avneesh Sud, Davide Cozzolino, Luisa Verdoliva
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18685-18694
affiliations: ["University Federico II of Naples", "Google DeepMind"]
cities: ["Naples", "Mountain View"]
countries: ["Italy", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guillaro_A_Bias-Free_Training_Paradigm_for_More_General_AI-generated_Image_Detection_CVPR_2025_paper.html

abstract:
Successful forensic detectors can produce excellent results in supervised learning benchmarks but struggle to transfer to real-world applications. We believe this limitation is largely due to inadequate training data quality. While most research focuses on developing new algorithms, less attention is given to training data selection, despite evidence that performance can be strongly impacted by spurious correlations such as content, format, or resolution. A well-designed forensic detector should detect generator specific artifacts rather than reflect data biases. To this end, we propose B-Free, a bias-free training paradigm, where fake images are generated from real ones using the conditioning procedure of stable diffusion models. This ensures semantic alignment between real and fake images, allowing any differences to stem solely from the subtle artifacts introduced by AI generation. Through content-based augmentation, we show significant improvements in both generalization and robustness over state-of-the-art detectors and more calibrated results across 27 different generative models, including recent releases, like FLUX and Stable Diffusion 3.5. Our findings emphasize the importance of a careful dataset design, highlighting the need for further research on this topic. Code and data are publicly available at https://grip-unina.github.io/B-Free/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guillaro_A_Bias-Free_Training_Paradigm_for_More_General_AI-generated_Image_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guillaro_A_Bias-Free_Training_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guillaro_2025_CVPR,
    author    = {Guillaro, Fabrizio and Zingarini, Giada and Usman, Ben and Sud, Avneesh and Cozzolino, Davide and Verdoliva, Luisa},
    title     = {A Bias-Free Training Paradigm for More General AI-generated Image Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18685-18694}
}

raw_affiliation:
[{"affiliation": "University Federico II of Naples", "city": "Naples", "country": "Italy"}, {"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}]


authors: Thanh-Dat Truong, Utsav Prabhu, Bhiksha Raj, Jackson Cothren, Khoa Luu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15065-15075
cities: ["Fayetteville", "Mountain View", "Pittsburgh", "Abu Dhabi", "Fayetteville"]
countries: ["USA", "USA", "USA", "UAE", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Truong_FALCON_Fairness_Learning_via_Contrastive_Attention_Approach_to_Continual_Semantic_CVPR_2025_paper.html

title:
FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding

abstract:
Continual Learning in semantic scene segmentation aims to continually learn new unseen classes in dynamic environments while maintaining previously learned knowledge. Prior studies focused on modeling the catastrophic forgetting and background shift challenges in continual learning. However, fairness, another major challenge that causes unfair predictions leading to low performance among major and minor classes, still needs to be well addressed. In addition, prior methods have yet to model the unknown classes well, thus resulting in producing non-discriminative features among unknown classes. This work presents a novel Fairness Learning via Contrastive Attention Approach to continual learning in semantic scene understanding. In particular, we first introduce a new Fairness Contrastive Clustering loss to address the problems of catastrophic forgetting and fairness. Then, we propose an attention-based visual grammar approach to effectively model the background shift problem and unknown classes, producing better feature representations for different unknown classes. Through our experiments, our proposed approach achieves State-of-the-Art (SoTA) performance on different continual learning benchmarks, i.e., ADE20K, Cityscapes, and Pascal VOC. It promotes the fairness of the continual semantic segmentation model.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Truong_FALCON_Fairness_Learning_via_Contrastive_Attention_Approach_to_Continual_Semantic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Truong_FALCON_Fairness_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Truong_2025_CVPR,
    author    = {Truong, Thanh-Dat and Prabhu, Utsav and Raj, Bhiksha and Cothren, Jackson and Luu, Khoa},
    title     = {FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15065-15075}
}

raw_affiliation:
[{"affiliation": "CVIU Lab, University of Arkansas, USA", "city": "Fayetteville", "country": "USA"}, {"affiliation": "Google DeepMind, USA", "city": "Mountain View", "country": "USA"}, {"affiliation": "Carnegie Mellon University, USA", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "Mohammed bin Zayed University of AI, UAE", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "Dep. of Geosciences, University of Arkansas, USA", "city": "Fayetteville", "country": "USA"}]

affiliations:
["CVIU Lab, University of Arkansas, USA", "Google DeepMind, USA", "Carnegie Mellon University, USA", "Mohammed bin Zayed University of AI, UAE", "Dep. of Geosciences, University of Arkansas, USA"]


title: Certified Human Trajectory Prediction
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12301-12311
affiliations: ["EPFL", "Apple"]
cities: ["Lausanne", "Cupertino"]
countries: ["Switzerland", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bahari_Certified_Human_Trajectory_Prediction_CVPR_2025_paper.html

authors:
Mohammadhossein Bahari, Saeed Saadatnejad, Amirhossein Askari Farsangi, Seyed-Mohsen Moosavi-Dezfooli, Alexandre Alahi

abstract:
Predicting human trajectories is essential for the safe operation of autonomous vehicles, yet current data-driven models often lack robustness in case of noisy inputs such as adversarial examples or imperfect observations. Although some trajectory prediction methods have been developed to provide empirical robustness, these methods are heuristic and do not offer guaranteed robustness. In this work, we propose a certification approach tailored for trajectory prediction that provides guaranteed robustness. To this end, we address the unique challenges associated with trajectory prediction, such as unbounded outputs and multi-modality. To mitigate the inherent performance drop through certification, we propose a diffusion-based trajectory denoiser and integrate it into our method. Moreover, we introduce new certified performance metrics to reliably measure the trajectory prediction performance. Through comprehensive experiments, we demonstrate the accuracy and robustness of the certified predictors and highlight their advantages over the non-certified ones. The code is available online: https://s-attack.github.io/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bahari_Certified_Human_Trajectory_Prediction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bahari_Certified_Human_Trajectory_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bahari_2025_CVPR,
    author    = {Bahari, Mohammadhossein and Saadatnejad, Saeed and Farsangi, Amirhossein Askari and Moosavi-Dezfooli, Seyed-Mohsen and Alahi, Alexandre},
    title     = {Certified Human Trajectory Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12301-12311}
}

raw_affiliation:
[{"affiliation": "EPFL", "city": "Lausanne", "country": "Switzerland"}, {"affiliation": "Apple", "city": "Cupertino", "country": "USA"}]


title: Evaluating Vision-Language Models as Evaluators in Path Planning
authors: Mohamed Aghzal, Xiang Yue, Erion Plaku, Ziyu Yao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6886-6897
affiliations: ["George Mason University", "Carnegie Mellon University", "National Science Foundation"]
cities: ["Fairfax", "Pittsburgh", "Alexandria"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Aghzal_Evaluating_Vision-Language_Models_as_Evaluators_in_Path_Planning_CVPR_2025_paper.html

abstract:
Despite their promise to perform complex reasoning, large language models (LLMs) have been shown to have limited effectiveness in end-to-end planning. This has inspired an intriguing question: if these models cannot plan well, can they still contribute to the planning framework as a helpful plan evaluator? In this work, we generalize this question to consider LLMs augmented with visual understanding, i.e., Vision-Language Models (VLMs). We introduce **PathEval**, a novel benchmark evaluating VLMs as plan evaluators in complex path-planning scenarios. Succeeding in the benchmark requires a VLM to be able to abstract traits of optimal paths from the scenario description, demonstrate precise low-level perception on each path, and integrate this information to decide the better path.  Our analysis of state-of-the-art VLMs reveals that these models face significant challenges on the benchmark. We observe that the VLMs can precisely abstract given scenarios to identify the desired traits and exhibit mixed performance in integrating the provided information. Yet, their vision component presents a critical bottleneck, with models struggling to perceive low-level details about a path. Our experimental results show that this issue cannot be trivially addressed via end-to-end fine-tuning; rather, task-specific discriminative adaptation of these vision encoders is needed for these VLMs to become effective path evaluators.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Aghzal_Evaluating_Vision-Language_Models_as_Evaluators_in_Path_Planning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Aghzal_Evaluating_Vision-Language_Models_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Aghzal_2025_CVPR,
    author    = {Aghzal, Mohamed and Yue, Xiang and Plaku, Erion and Yao, Ziyu},
    title     = {Evaluating Vision-Language Models as Evaluators in Path Planning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6886-6897}
}

raw_affiliation:
[{"affiliation": "George Mason University", "city": "Fairfax", "country": "USA"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "National Science Foundation", "city": "Alexandria", "country": "USA"}]


title: Free on the Fly: Enhancing Flexibility in Test-Time Adaptation with Online EM
authors: Qiyuan Dai, Sibei Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9538-9548
affiliations: ["School of Information Science and Technology, ShanghaiTech University", "Sun Yat-sen University"]
cities: ["Shanghai", "Guangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dai_Free_on_the_Fly_Enhancing_Flexibility_in_Test-Time_Adaptation_with_CVPR_2025_paper.html

abstract:
Vision-Language Models (VLMs) have become prominent in open-world image recognition for their strong generalization abilities. Yet, their effectiveness in practical applications is compromised by domain shifts and distributional changes, especially when test data distributions diverge from training data. Therefore, the paradigm of test-time adaptation (TTA) has emerged, enabling the use of online off-the-shelf data at test time, supporting independent sample predictions, and eliminating reliance on test annotations. Traditional TTA methods, however, often rely on costly training or optimization processes, or make unrealistic assumptions about accessing or storing historical training and test data.Instead, this study proposes FreeTTA, a training-free and universally available method that makes no assumptions, to enhance the flexibility of TTA. More importantly, FreeTTA is the first to explicitly model the test data distribution, enabling the use of intrinsic relationships among test samples to enhance predictions of individual samples without simultaneous access--a direction not previously explored. FreeTTA achieves these advantages by introducing an online EM algorithm that utilizes zero-shot predictions from VLMs as priors to iteratively compute the posterior probabilities of each online test sample and update parameters. Experiments demonstrate that FreeTTA achieves stable and significant improvements compared to state-of-the-art methods across 15 datasets in both cross-domain and out-of-distribution settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dai_Free_on_the_Fly_Enhancing_Flexibility_in_Test-Time_Adaptation_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dai_Free_on_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dai_2025_CVPR,
    author    = {Dai, Qiyuan and Yang, Sibei},
    title     = {Free on the Fly: Enhancing Flexibility in Test-Time Adaptation with Online EM},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9538-9548}
}

raw_affiliation:
[{"affiliation": "School of Information Science and Technology, ShanghaiTech University", "city": "Shanghai", "country": "China"}, {"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}]


title: Transformers without Normalization
authors: Jiachen Zhu, Xinlei Chen, Kaiming He, Yann LeCun, Zhuang Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14901-14911
affiliations: ["FAIR, Meta", "New York University", "MIT", "Princeton University"]
cities: ["Menlo Park", "New York", "Cambridge", "Princeton"]
countries: ["USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Transformers_without_Normalization_CVPR_2025_paper.html

abstract:
Normalization layers are ubiquitous in modern neural networks and have long been considered essential. This work demonstrates that Transformers without normalization can achieve the same or better performance using a remarkably simple technique. We introduce Dynamic Tanh (DyT), an element-wise operation DyT(x)=tanh(ax), as a drop-in replacement for normalization layers in Transformers. DyT is inspired by the observation that layer normalization in Transformers often produces tanh-like, S-shaped input-output mappings. By incorporating DyT, Transformers without normalization can match or exceed the performance of their normalized counterparts, mostly without hyperparameter tuning. We validate the effectiveness of Transformers with DyT across diverse settings, ranging from recognition to generation, supervised to self-supervised learning, and computer vision to language models. These findings challenge the conventional understanding that normalization layers are indispensable in modern neural networks, and offer new insights into their role in deep networks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Transformers_without_Normalization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Transformers_without_Normalization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Jiachen and Chen, Xinlei and He, Kaiming and LeCun, Yann and Liu, Zhuang},
    title     = {Transformers without Normalization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14901-14911}
}

raw_affiliation:
[{"affiliation": "FAIR, Meta", "city": "Menlo Park", "country": "USA"}, {"affiliation": "New York University", "city": "New York", "country": "USA"}, {"affiliation": "MIT", "city": "Cambridge", "country": "USA"}, {"affiliation": "Princeton University", "city": "Princeton", "country": "USA"}]


title: SGC-Net: Stratified Granular Comparison Network for Open-Vocabulary HOI Detection
authors: Xin Lin, Chong Shi, Zuopeng Yang, Haojin Tang, Zhili Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4539-4549
cities: ["unknown", "unknown", "unknown", "unknown", "unknown"]
countries: ["unknown", "unknown", "unknown", "unknown", "unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_SGC-Net_Stratified_Granular_Comparison_Network_for_Open-Vocabulary_HOI_Detection_CVPR_2025_paper.html

abstract:
Recent open-vocabulary human-object interaction (OV-HOI) detection methods primarily rely on large language model (LLM) for generating auxiliary descriptions and leverage knowledge distilled from CLIP to detect unseen interaction categories. Despite their effectiveness, these methods face two challenges: (1) feature granularity deficiency, due to reliance on last layer visual features for text alignment, leading to the neglect of crucial object-level details from intermediate layers; (2) semantic similarity confusion, resulting from CLIP's inherent biases toward certain classes, while LLM-generated descriptions based solely on labels fail to adequately capture inter-class similarities. To address these challenges, we propose a stratified granular comparison network. First, we introduce a granularity sensing alignment module that aggregates global semantic features with local details, refining interaction representations and ensuring robust alignment between intermediate visual features and text embeddings. Second, we develop a hierarchical group comparison module that recursively compares and groups classes using LLMs, generating fine-grained and discriminative descriptions for each interaction category. Experimental results on two widely-used benchmark datasets, SWIG-HOI and HICO-DET, demonstrate that our method achieves state-of-the-art results in OV-HOI detection. Codes is available at https://github.com/Phil0212/SGC-Net.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_SGC-Net_Stratified_Granular_Comparison_Network_for_Open-Vocabulary_HOI_Detection_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Xin and Shi, Chong and Yang, Zuopeng and Tang, Haojin and Zhou, Zhili},
    title     = {SGC-Net: Stratified Granular Comparison Network for Open-Vocabulary HOI Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4539-4549}
}

raw_affiliation:
[{"affiliation": "\u00dc\u00bb\u00ac\u00bb\u00bd\u00ac\u00b7\u00b1\u00b2", "city": "unknown", "country": "unknown"}, {"affiliation": "\u00c8\u00b7\u00b2\u00d4\u00b7\u00b2 \u00dd\u00b8\u00b1\u00b2\u00b9 \u00cd\u00b8\u00b7 \u00c6\u00ab\u00b1\u00b0\u00bb\u00b2\u00b9 \u00c7\u00bf\u00b2\u00b9", "city": "unknown", "country": "unknown"}, {"affiliation": "\u00cc\u00bf\u00b2\u00b9", "city": "unknown", "country": "unknown"}, {"affiliation": "\u00c6\u00b8\u00b7\u00b4\u00b7 \u00c6\u00b8\u00b1\u00ab \u00d9\u00ab\u00bf\u00b2\u00b9\u00a6\u00b8\u00b1\u00ab \u00cb\u00b2\u00b7\u00aa\u00bb\u00ae\u00ad\u00b7\u00ac\u00a7", "city": "unknown", "country": "unknown"}, {"affiliation": "\u00a6\u00b8\u00b7\u00b4\u00b7\u00e0\u00ef\u00ea\u00ed\u00f2\u00bd\u00b1\u00b3", "city": "unknown", "country": "unknown"}]

affiliations:
["\u00dc\u00bb\u00ac\u00bb\u00bd\u00ac\u00b7\u00b1\u00b2", "\u00c8\u00b7\u00b2\u00d4\u00b7\u00b2 \u00dd\u00b8\u00b1\u00b2\u00b9 \u00cd\u00b8\u00b7 \u00c6\u00ab\u00b1\u00b0\u00bb\u00b2\u00b9 \u00c7\u00bf\u00b2\u00b9", "\u00cc\u00bf\u00b2\u00b9", "\u00c6\u00b8\u00b7\u00b4\u00b7 \u00c6\u00b8\u00b1\u00ab \u00d9\u00ab\u00bf\u00b2\u00b9\u00a6\u00b8\u00b1\u00ab \u00cb\u00b2\u00b7\u00aa\u00bb\u00ae\u00ad\u00b7\u00ac\u00a7", "\u00a6\u00b8\u00b7\u00b4\u00b7\u00e0\u00ef\u00ea\u00ed\u00f2\u00bd\u00b1\u00b3"]


title: Galaxy Walker: Geometry-aware VLMs For Galaxy-scale Understanding
authors: Tianyu Chen, Xingcheng Fu, Yisen Gao, Haodong Qian, Yuecen Wei, Kun Yan, Haoyi Zhou, Jianxin Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4112-4121
cities: ["Beijing", "Beijing", "Guilin", "Beijing"]
countries: ["China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Galaxy_Walker_Geometry-aware_VLMs_For_Galaxy-scale_Understanding_CVPR_2025_paper.html

abstract:
Modern vision-language models (VLMs) develop patch embedding and convolution backbone within vector space, especially Euclidean ones, at the very founding. When expanding VLMs to a galaxy-scale for understanding astronomical phenomena, the integration of spherical space for planetary orbits and hyperbolic spaces for black holes raises two formidable challenges. a) The current pre-training model is confined to Euclidean space rather than a comprehensive geometric embedding. b) The predominant architecture lacks suitable backbones for anisotropic physical geometries. In this paper, we introduced Galaxy-Walker, a geometry-aware VLM, for the universe-level vision understanding tasks. We proposed the geometry prompt that generates geometry tokens by random walks across diverse spaces on a multi-scale physical graph, along with a geometry adapter that compresses and reshapes the space anisotropy in a mixture-of-experts manner. Extensive experiments demonstrate the effectiveness of our approach, with Galaxy-Walker achieving state-of-the-art performance in both galaxy property estimation (R2 scores up to 0.91) and morphology classification tasks (up to +0.17 F1 improvement in challenging features), significantly outperforming both domain-specific models and general-purpose VLMs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Galaxy_Walker_Geometry-aware_VLMs_For_Galaxy-scale_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Galaxy_Walker_Geometry-aware_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Tianyu and Fu, Xingcheng and Gao, Yisen and Qian, Haodong and Wei, Yuecen and Yan, Kun and Zhou, Haoyi and Li, Jianxin},
    title     = {Galaxy Walker: Geometry-aware VLMs For Galaxy-scale Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4112-4121}
}

raw_affiliation:
[{"affiliation": "SKLCCSE, School of Computer Science and Engineering, Beihang University, China\u22c6", "city": "Beijing", "country": "China"}, {"affiliation": "School of Software, Beihang University, China\u2021", "city": "Beijing", "country": "China"}, {"affiliation": "Key Lab of Education Blockchain and Intelligent Technology, Guangxi Normal University, China\u22c4", "city": "Guilin", "country": "China"}, {"affiliation": "Institute of Artificial Intelligence, Beihang University, Beijing, China\u2020", "city": "Beijing", "country": "China"}]

affiliations:
["SKLCCSE, School of Computer Science and Engineering, Beihang University, China\u22c6", "School of Software, Beihang University, China\u2021", "Key Lab of Education Blockchain and Intelligent Technology, Guangxi Normal University, China\u22c4", "Institute of Artificial Intelligence, Beihang University, Beijing, China\u2020"]


title: HiPART: Hierarchical Pose AutoRegressive Transformer for Occluded 3D Human Pose Estimation
authors: Hongwei Zheng, Han Li, Wenrui Dai, Ziyang Zheng, Chenglin Li, Junni Zou, Hongkai Xiong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16807-16817
raw_affiliation: [{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]
affiliations: ["Shanghai Jiao Tong University"]
cities: ["Shanghai"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_HiPART_Hierarchical_Pose_AutoRegressive_Transformer_for_Occluded_3D_Human_Pose_CVPR_2025_paper.html

abstract:
Existing 2D-to-3D human pose estimation (HPE) methods struggle with the occlusion issue by enriching information like temporal and visual cues in the lifting stage. In this paper, we argue that these methods ignore the limitation of the sparse skeleton 2D input representation, which fundamentally restricts the 2D-to-3D lifting and worsens the occlusion issue. To address these, we propose a novel two-stage generative densification method, named Hierarchical Pose AutoRegressive Transformer (HiPART), to generate  hierarchical 2D dense poses from the original sparse 2D pose. Specifically, we first develop a multi-scale skeleton tokenization module to quantize the highly dense 2D pose into hierarchical tokens and propose a skeleton-aware alignment to strengthen token connections. We then develop a hierarchical autoregressive modeling scheme for hierarchical 2D pose generation. With generated hierarchical poses as inputs for 2D-to-3D lifting, the proposed method shows strong robustness in occluded scenarios and achieves state-of-the-art performance on the single-frame-based 3D HPE. Moreover, it outperforms numerous multi-frame methods while reducing parameter and computational complexity and can also complement them to further enhance performance and robustness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_HiPART_Hierarchical_Pose_AutoRegressive_Transformer_for_Occluded_3D_Human_Pose_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_HiPART_Hierarchical_Pose_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zheng_2025_CVPR,
    author    = {Zheng, Hongwei and Li, Han and Dai, Wenrui and Zheng, Ziyang and Li, Chenglin and Zou, Junni and Xiong, Hongkai},
    title     = {HiPART: Hierarchical Pose AutoRegressive Transformer for Occluded 3D Human Pose Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16807-16817}
}


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4302-4312
cities: ["Guangzhou", "Xiamen", "Chongqing", "Hong Kong", "Hong Kong"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lai_SnowMaster_Comprehensive_Real-world_Image_Desnowing_via_MLLM_with_Multi-Model_Feedback_CVPR_2025_paper.html

title:
SnowMaster: Comprehensive Real-world Image Desnowing via MLLM with Multi-Model Feedback Optimization

authors:
Jianyu Lai, Sixiang Chen, Yunlong Lin, Tian Ye, Yun Liu, Song Fei, Zhaohu Xing, Hongtao Wu, Weiming Wang, Lei Zhu

abstract:
Snowfall presents significant challenges for visual data processing, necessitating specialized desnowing algorithms. However, existing models often fail to generalize effectively due to their heavy reliance on synthetic datasets. Furthermore, current real-world snowfall datasets are limited in scale and lack dedicated evaluation metrics designed specifically for snowfall degradation, thus hindering the effective integration of real snowy images into model training to reduce domain gaps. To address these challenges, we first introduce RealSnow10K, a large-scale, high-quality dataset consisting of over 10,000 annotated real-world snowy images. In addition, we curate a preference dataset comprising 36,000 expert-ranked image pairs, enabling the adaptation of multimodal large language models (MLLMs) to better perceive snowy image quality through our innovative Multi-Model Preference Optimization (MMPO). Finally, we propose the SnowMaster, which employs MMPO-enhanced MLLM to perform accurate snowy image evaluation and pseudo-label filtering for semi-supervised training. Experiments demonstrate that SnowMaster delivers superior desnowing performance under real-world conditions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lai_SnowMaster_Comprehensive_Real-world_Image_Desnowing_via_MLLM_with_Multi-Model_Feedback_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lai_SnowMaster_Comprehensive_Real-world_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lai_2025_CVPR,
    author    = {Lai, Jianyu and Chen, Sixiang and Lin, Yunlong and Ye, Tian and Liu, Yun and Fei, Song and Xing, Zhaohu and Wu, Hongtao and Wang, Weiming and Zhu, Lei},
    title     = {SnowMaster: Comprehensive Real-world Image Desnowing via MLLM with Multi-Model Feedback Optimization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4302-4312}
}

raw_affiliation:
[{"affiliation": "The Hong Kong University of Science and Technology (GZ)", "city": "Guangzhou", "country": "China"}, {"affiliation": "Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "Southwestern University", "city": "Chongqing", "country": "China"}, {"affiliation": "Hong Kong Metropolitan University", "city": "Hong Kong", "country": "China"}, {"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}]

affiliations:
["The Hong Kong University of Science and Technology (GZ)", "Xiamen University", "Southwestern University", "Hong Kong Metropolitan University", "The Hong Kong University of Science and Technology"]


title: From Faces to Voices: Learning Hierarchical Representations for High-quality Video-to-Speech
authors: Ji-Hoon Kim, Jeongsoo Choi, Jaehun Kim, Chaeyoung Jung, Joon Son Chung
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15874-15884
affiliations: ["Korea Advanced Institute of Science and Technology"]
cities: ["Daejeon"]
countries: ["South Korea"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_From_Faces_to_Voices_Learning_Hierarchical_Representations_for_High-quality_Video-to-Speech_CVPR_2025_paper.html

abstract:
The objective of this study is to generate high-quality speech from silent talking face videos, a task also known as video-to-speech synthesis. A significant challenge in video-to-speech synthesis lies in the substantial modality gap between silent video and multi-faceted speech.  In this paper, we propose a novel video-to-speech system that effectively bridges this modality gap, significantly enhancing the quality of synthesized speech. This is achieved by learning of hierarchical representations from video to speech. Specifically, we gradually transform silent video into acoustic feature spaces through three sequential stages -- content, timbre, and prosody modeling. In each stage, we align visual factors -- lip movements, face identity, and facial expressions -- with corresponding acoustic counterparts to ensure the seamless transformation. Additionally, to generate realistic and coherent speech from the visual representations, we employ a flow matching model that estimates direct trajectories from a simple prior distribution to the target speech distribution. Extensive experiments demonstrate that our method achieves exceptional generation quality comparable to real utterances, outperforming existing methods by a significant margin.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_From_Faces_to_Voices_Learning_Hierarchical_Representations_for_High-quality_Video-to-Speech_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Ji-Hoon and Choi, Jeongsoo and Kim, Jaehun and Jung, Chaeyoung and Chung, Joon Son},
    title     = {From Faces to Voices: Learning Hierarchical Representations for High-quality Video-to-Speech},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15874-15884}
}

raw_affiliation:
[{"affiliation": "Korea Advanced Institute of Science and Technology", "city": "Daejeon", "country": "South Korea"}]


title: DFM: Differentiable Feature Matching for Anomaly Detection
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15224-15233
cities: ["Beijing", "Beijing", "Beijing", "Morgantown", "Buffalo", "Hangzhou", "Beijing", "Nanchang"]
countries: ["China", "China", "China", "USA", "USA", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_DFM_Differentiable_Feature_Matching_for_Anomaly_Detection_CVPR_2025_paper.html

authors:
Sheng Wu, Yimi Wang, Xudong Liu, Yuguang Yang, Runqi Wang, Guodong Guo, David Doermann, Baochang Zhang

abstract:
Feature matching methods for unsupervised anomaly detection have demonstrated impressive performance. Existing methods primarily rely on self-supervised training and handcrafted matching schemes for task adaptation. However, they can only achieve an inferior feature representation for anomaly detection because the feature extraction and matching modules are separately trained. To address these issues, we propose a Differentiable Feature Matching  (DFM) framework for joint optimization of the feature extractor and the matching head. DFM transforms nearest-neighbor matching into a pooling-based module and embeds it within a Feature Matching Network (FMN). This design enables end-to-end feature extraction and feature matching module training, thus providing better feature representation for anomaly detection tasks. DFM is generic and can be incorporated into existing feature-matching methods. We implement DFM with various backbones and conduct extensive experiments across various tasks and datasets, demonstrating its effectiveness. Notably, we achieve state-of-the-art results in the continual anomaly detection task with instance-AUROC improvement of up to 3.9% and pixel-AP improvement of up to 5.5%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_DFM_Differentiable_Feature_Matching_for_Anomaly_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_DFM_Differentiable_Feature_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Sheng and Wang, Yimi and Liu, Xudong and Yang, Yuguang and Wang, Runqi and Guo, Guodong and Doermann, David and Zhang, Baochang},
    title     = {DFM: Differentiable Feature Matching for Anomaly Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15224-15233}
}

raw_affiliation:
[{"affiliation": "School of Computer Science & Technology, Beijing Jiaotong University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Arti\ufb01cial Intelligence, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Electronic Information Engineering, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "West Virginia University", "city": "Morgantown", "country": "USA"}, {"affiliation": "State University of New Y ork at Buffalo", "city": "Buffalo", "country": "USA"}, {"affiliation": "Hangzhou Research Institute, Beihang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Zhongguancun Laboratory", "city": "Beijing", "country": "China"}, {"affiliation": "Nanchang Institute of Technology, Nanchang", "city": "Nanchang", "country": "China"}]

affiliations:
["School of Computer Science & Technology, Beijing Jiaotong University", "School of Arti\ufb01cial Intelligence, Beihang University", "School of Electronic Information Engineering, Beihang University", "West Virginia University", "State University of New Y ork at Buffalo", "Hangzhou Research Institute, Beihang University", "Zhongguancun Laboratory", "Nanchang Institute of Technology, Nanchang"]


title: FlashGS: Efficient 3D Gaussian Splatting for Large-scale and High-resolution Rendering
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26652-26662
cities: ["Shanghai", "Beijing", "Beijing", "Hong Kong", "Hong Kong", "Beijing", "Unknown"]
countries: ["China", "China", "China", "China", "China", "China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Feng_FlashGS_Efficient_3D_Gaussian_Splatting_for_Large-scale_and_High-resolution_Rendering_CVPR_2025_paper.html

authors:
Guofeng Feng, Siyan Chen, Rong Fu, Zimu Liao, Yi Wang, Tao Liu, Boni Hu, Linning Xu, Zhilin Pei, Hengjie Li, Xiuhong Li, Ninghui Sun, Xingcheng Zhang, Bo Dai

abstract:
Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated significant potential over traditional rendering techniques, attracting widespread attention from both industry and academia. However, real-time rendering with 3DGS remains a challenging problem, particularly in large-scale, high-resolution scenes due to the presence of numerous anisotropic Gaussian representations, and it has not been extensively explored. To address this challenge, we introduce FlashGS, an open-source CUDA library with Python bindings, featuring comprehensive algorithm design and optimizations, including redundancy elimination, adaptive scheduling, and efficient pipelining. First, we eliminate substantial redundant computations through precise Gaussian intersection tests, leveraging the intrinsic mechanism of the 3DGS rasterizer. During task partitioning, we propose an adaptive scheduling strategy that accounts for variations in Gaussian size and shape. Additionally, we design a multi-stage pipelining strategy for color computation in the rendering process, further accelerating performance. We conduct an extensive evaluation of FlashGS across a diverse range of synthetic and real-world 3D scenes, encompassing scene sizes of up to 2.7 km^2 cityscape and resolutions of up to over 4K. Our approach improves 3DGS rendering performance by an order of magnitude, achieving an average speedup of 7.2x, and rendering at a minimum of 125.9 FPS, setting a new state-of-the-art in real-time 3DGS rendering. https://github.com/InternLandMark/FlashGS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_FlashGS_Efficient_3D_Gaussian_Splatting_for_Large-scale_and_High-resolution_Rendering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Feng_FlashGS_Efficient_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Feng_2025_CVPR,
    author    = {Feng, Guofeng and Chen, Siyan and Fu, Rong and Liao, Zimu and Wang, Yi and Liu, Tao and Hu, Boni and Xu, Linning and Pei, Zhilin and Li, Hengjie and Li, Xiuhong and Sun, Ninghui and Zhang, Xingcheng and Dai, Bo},
    title     = {FlashGS: Efficient 3D Gaussian Splatting for Large-scale and High-resolution Rendering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26652-26662}
}

raw_affiliation:
[{"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Institute of Computing Technology, CAS", "city": "Beijing", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Independent Researcher", "city": "Unknown", "country": "Unknown"}]

affiliations:
["Shanghai Artificial Intelligence Laboratory", "Institute of Computing Technology, CAS", "University of Chinese Academy of Sciences", "The Chinese University of Hong Kong", "The University of Hong Kong", "Peking University", "Independent Researcher"]


title: PointSR: Self-Regularized Point Supervision for Drone-View Object Detection
authors: Weizhuo Li, Yue Xi, Wenjing Jia, Zehao Zhang, Fei Li, Xiangzeng Liu, Qiguang Miao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11707-11716
cities: ["Guangzhou", "Sydney", "Xi\u2019an"]
countries: ["China", "Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_PointSR_Self-Regularized_Point_Supervision_for_Drone-View_Object_Detection_CVPR_2025_paper.html

abstract:
Point-Supervised Object Detection (PSOD) in a discriminative style has recently gained significant attention for its impressive detection performance and cost-effectiveness. However, accurately predicting high-quality pseudo-box labels for drone-view images, which often feature densely packed small objects, remains a challenge. This difficulty arises primarily from the limitation of rigid sampling strategies, which hinder the pseudo-box optimization process. To address this, we propose PointSR, an effective and robust point-supervised object detection framework with self-regularized sampling that integrates temporal and informative constraints throughout the pseudo-box generation process. Specifically, the framework comprises three key components: Temporal-Ensembling Encoder (TE Encoder), Coarse Pseudo-box Prediction, and Pseudo-box Refinement. The TE Encoder builds an anchor prototype library by aggregating temporal information for dynamic anchor adjustment. In Coarse Pseudo-box Prediction, anchors are refined using the prototype library, and a set of informative samples is collected for subsequent refinement. During Pseudo-box Refinement, these informative negative samples are used to suppress low-confidence candidate positive samples, thereby improving the quality of the pseudo-boxes. Experimental results on benchmark datasets demonstrate that PointSR significantly outperforms state-of-the-art methods, achieving up to 2.6%~\mathbf 7.2%  higher AP_ 50   using only point supervision. Additionally, it exhibits strong robustness to perturbation in human-labeled points.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_PointSR_Self-Regularized_Point_Supervision_for_Drone-View_Object_Detection_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Weizhuo and Xi, Yue and Jia, Wenjing and Zhang, Zehao and Li, Fei and Liu, Xiangzeng and Miao, Qiguang},
    title     = {PointSR: Self-Regularized Point Supervision for Drone-View Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11707-11716}
}

raw_affiliation:
[{"affiliation": "Guangzhou Institute of Technology, Xidian University", "city": "Guangzhou", "country": "China"}, {"affiliation": "University of Technology Sydney", "city": "Sydney", "country": "Australia"}, {"affiliation": "Xidian University", "city": "Xi\u2019an", "country": "China"}]

affiliations:
["Guangzhou Institute of Technology, Xidian University", "University of Technology Sydney", "Xidian University"]


title: Exploring Timeline Control for Facial Motion Generation
authors: Yifeng Ma, Jinwei Qi, Chaonan Ji, Peng Zhang, Bang Zhang, Zhidong Deng, Liefeng Bo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1940-1950
affiliations: ["Department of Computer Science and Technology, Tsinghua University", "Tongyi Lab, Alibaba Group"]
cities: ["Beijing", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Exploring_Timeline_Control_for_Facial_Motion_Generation_CVPR_2025_paper.html

abstract:
This paper introduces a new control signal for facial motion generation: timeline control. Compared to audio and text signals, timelines provide more fine-grained control, such as generating specific facial motions with precise timing. Users can specify a multi-track timeline of facial actions arranged in temporal intervals, allowing precise control over the timing of each action. To model the timeline control capability, We first annotate the time intervals of facial actions in natural facial motion sequences at a frame-level granularity. This process is facilitated by Toeplitz Inverse Covariance-based Clustering to minimize human labor. Based on the annotations, we propose a diffusion-based generation model capable of generating facial motions that are natural and accurately aligned with input timelines. Our method supports text-guided motion generation by using ChatGPT to convert text into timelines. Experimental results show that our method can annotate facial action intervals with satisfactory accuracy, and produces natural facial motions accurately aligned with timelines.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_Exploring_Timeline_Control_for_Facial_Motion_Generation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Yifeng and Qi, Jinwei and Ji, Chaonan and Zhang, Peng and Zhang, Bang and Deng, Zhidong and Bo, Liefeng},
    title     = {Exploring Timeline Control for Facial Motion Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1940-1950}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science and Technology, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Tongyi Lab, Alibaba Group", "city": "Hangzhou", "country": "China"}]


title: v-CLR: View-Consistent Learning for Open-World Instance Segmentation
authors: Chang-Bin Zhang, Jinhong Ni, Yujie Zhong, Kai Han
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20307-20317
affiliations: ["Visual AI Lab, The University of Hong Kong", "Meituan Inc."]
cities: ["Hong Kong", "Beijing"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_v-CLR_View-Consistent_Learning_for_Open-World_Instance_Segmentation_CVPR_2025_paper.html

abstract:
In this paper, we address the challenging problem of open-world instance segmentation. Existing works have shown that vanilla visual networks are biased toward learning appearance information, e.g. texture, to recognize objects. This implicit bias causes the model to fail in detecting novel objects with unseen textures in the open-world setting. To address this challenge, we propose a learning framework, called view-Consistent LeaRning (v-CLR), which aims to enforce the model to learn appearance-invariant representations for robust instance segmentation. In v-CLR, we first introduce additional views for each image, where the texture undergoes significant alterations while preserving the image's underlying structure. We then encourage the model to learn the appearance-invariant representation by enforcing the consistency between object features across different views, for which we obtain class-agnostic object proposals using off-the-shelf unsupervised models that possess strong object-awareness. These proposals enable cross-view object feature matching, greatly reducing the appearance dependency while enhancing the object-awareness. We thoroughly evaluate our method on public benchmarks under both cross-class and cross-dataset settings, achieving state-of-the-art performance.  Project page: https://visual-ai.github.io/vclr

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_v-CLR_View-Consistent_Learning_for_Open-World_Instance_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_v-CLR_View-Consistent_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Chang-Bin and Ni, Jinhong and Zhong, Yujie and Han, Kai},
    title     = {v-CLR: View-Consistent Learning for Open-World Instance Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20307-20317}
}

raw_affiliation:
[{"affiliation": "Visual AI Lab, The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Meituan Inc.", "city": "Beijing", "country": "China"}]


title: Chat2SVG: Vector Graphics Generation with Large Language Models and Image Diffusion Models
authors: Ronghuan Wu, Wanchao Su, Jing Liao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23690-23700
affiliations: ["City University of Hong Kong", "Monash University", "City University of Hong Kong"]
cities: ["Hong Kong", "Melbourne", "Hong Kong"]
countries: ["China", "Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Chat2SVG_Vector_Graphics_Generation_with_Large_Language_Models_and_Image_CVPR_2025_paper.html

abstract:
Scalable Vector Graphics (SVG) has become the de facto standard for vector graphics in digital design, offering resolution independence and precise control over individual elements. Despite their advantages, creating high-quality SVG content remains challenging, as it demands technical expertise with professional editing software and a considerable time investment to craft complex shapes. Recent text-to-SVG generation methods aim to make vector graphics creation more accessible, but they still encounter limitations in shape regularity, generalization ability, and expressiveness. To address these challenges, we introduce Chat2SVG, a hybrid framework that combines the strengths of Large Language Models (LLMs) and image diffusion models for text-to-SVG generation. Our approach first uses an LLM to generate semantically meaningful SVG templates from basic geometric primitives. Guided by image diffusion models, a dual-stage optimization pipeline refines paths in latent space and adjusts point coordinates to enhance geometric complexity. Extensive experiments show that Chat2SVG outperforms existing methods in visual fidelity, path regularity, and semantic alignment. Additionally, our system enables intuitive editing through natural language instructions, making professional vector graphics creation accessible to all users. Our code is available at https://chat2svg.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Chat2SVG_Vector_Graphics_Generation_with_Large_Language_Models_and_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Chat2SVG_Vector_Graphics_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Ronghuan and Su, Wanchao and Liao, Jing},
    title     = {Chat2SVG: Vector Graphics Generation with Large Language Models and Image Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23690-23700}
}

raw_affiliation:
[{"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Monash University", "city": "Melbourne", "country": "Australia"}, {"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}]


title: GAF: Gaussian Avatar Reconstruction from Monocular Videos via Multi-view Diffusion
authors: Jiapeng Tang, Davide Davoli, Tobias Kirschstein, Liam Schoneveld, Matthias Nießner
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5546-5558
affiliations: ["Technical University of Munich", "Toyota Motor Europe NV/SA", "Woven by Toyota"]
cities: ["Munich", "Brussels", "Tokyo"]
countries: ["Germany", "Belgium", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_GAF_Gaussian_Avatar_Reconstruction_from_Monocular_Videos_via_Multi-view_Diffusion_CVPR_2025_paper.html

abstract:
We propose a novel approach for reconstructing animatable 3D Gaussian avatars from monocular videos captured by commodity devices like smartphones. Photorealistic 3D head avatar reconstruction from such recordings is challenging due to limited observations, which leaves unobserved regions under-constrained and can lead to artifacts in novel views. To address this problem, we introduce a multi-view head diffusion model, leveraging its priors to fill in missing regions and ensure view consistency in Gaussian splatting renderings. To enable precise viewpoint control, we use normal maps rendered from FLAME-based head reconstruction, which provides pixel-aligned inductive biases. We also condition the diffusion model on VAE features extracted from the input image to preserve facial identity and appearance details. For Gaussian avatar reconstruction, we distill multi-view diffusion priors by using iteratively denoised images as pseudo-ground truths, effectively mitigating over-saturation issues.  To further improve photorealism, we apply latent upsampling priors to refine the denoised latent before decoding it into an image. We evaluate our method on the NeRSemble dataset, showing that GAF outperforms previous state-of-the-art methods in novel view synthesis. Furthermore, we demonstrate higher-fidelity avatar reconstructions from monocular videos captured on commodity devices.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_GAF_Gaussian_Avatar_Reconstruction_from_Monocular_Videos_via_Multi-view_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_GAF_Gaussian_Avatar_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Jiapeng and Davoli, Davide and Kirschstein, Tobias and Schoneveld, Liam and Nie{\ss}ner, Matthias},
    title     = {GAF: Gaussian Avatar Reconstruction from Monocular Videos via Multi-view Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5546-5558}
}

raw_affiliation:
[{"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Toyota Motor Europe NV/SA", "city": "Brussels", "country": "Belgium"}, {"affiliation": "Woven by Toyota", "city": "Tokyo", "country": "Japan"}]


authors: Siyan Dong, Shuzhe Wang, Shaohui Liu, Lulu Cai, Qingnan Fan, Juho Kannala, Yanchao Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16739-16752
affiliations: ["The University of Hong Kong", "Aalto University", "ETH Zurich", "VIVO", "University of Oulu"]
cities: ["Hong Kong", "Espoo", "Zurich", "Unknown", "Oulu"]
countries: ["China", "Finland", "Switzerland", "Unknown", "Finland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dong_Reloc3r_Large-Scale_Training_of_Relative_Camera_Pose_Regression_for_Generalizable_CVPR_2025_paper.html

title:
Reloc3r: Large-Scale Training of Relative Camera Pose Regression for Generalizable, Fast, and Accurate Visual Localization

abstract:
Visual localization aims to determine the camera pose of a query image relative to a database of posed images. In recent years, deep neural networks that directly regress camera poses have gained popularity due to their fast inference capabilities. However, existing methods struggle to either generalize well to new scenes or provide accurate camera pose estimates. To address these issues, we present Reloc3r, a simple yet effective visual localization framework. It consists of an elegantly designed relative pose regression network, and a minimalist motion averaging module for absolute pose estimation. Trained on approximately eight million posed image pairs, Reloc3r achieves surprisingly good performance and generalization ability. We conduct extensive experiments on six public datasets, consistently demonstrating the effectiveness and efficiency of the proposed method. It provides high-quality camera pose estimates in real time and generalizes to novel scenes. Code: https://github.com/ffrivera0/reloc3r.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dong_Reloc3r_Large-Scale_Training_of_Relative_Camera_Pose_Regression_for_Generalizable_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dong_Reloc3r_Large-Scale_Training_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dong_2025_CVPR,
    author    = {Dong, Siyan and Wang, Shuzhe and Liu, Shaohui and Cai, Lulu and Fan, Qingnan and Kannala, Juho and Yang, Yanchao},
    title     = {Reloc3r: Large-Scale Training of Relative Camera Pose Regression for Generalizable, Fast, and Accurate Visual Localization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16739-16752}
}

raw_affiliation:
[{"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Aalto University", "city": "Espoo", "country": "Finland"}, {"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "VIVO", "city": "Unknown", "country": "Unknown"}, {"affiliation": "University of Oulu", "city": "Oulu", "country": "Finland"}]


title: AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark
authors: Li Lin, Santosh Santosh, Mingyang Wu, Xin Wang, Shu Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3503-3515
affiliations: ["Purdue University", "University at Albany, State University of New York"]
cities: ["West Lafayette", "New York"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_AI-Face_A_Million-Scale_Demographically_Annotated_AI-Generated_Face_Dataset_and_Fairness_CVPR_2025_paper.html

abstract:
AI-generated faces have enriched human life, such as entertainment, education, and art. However, they also pose misuse risks. Therefore, detecting AI-generated faces becomes crucial, yet current detectors show biased performance across different demographic groups. Mitigating biases can be done by designing algorithmic fairness methods, which usually require demographically annotated face datasets for model training. However, no existing dataset encompasses both demographic attributes and diverse generative methods simultaneously, which hinders the development of fair detectors for AI-generated faces. In this work, we introduce the AI-Face dataset, the first million-scale demographically annotated AI-generated face image dataset, including real faces, faces from deepfake videos, and faces generated by Generative Adversarial Networks and Diffusion Models. Based on this dataset, we conduct the first comprehensive fairness benchmark to assess various AI face detectors and provide valuable insights and findings to promote the future fair design of AI face detectors. Our AI-Face dataset and benchmark code are publicly available at https://github.com/Purdue-M2/AI-Face-FairnessBench.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_AI-Face_A_Million-Scale_Demographically_Annotated_AI-Generated_Face_Dataset_and_Fairness_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_AI-Face_A_Million-Scale_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Li and Santosh, Santosh and Wu, Mingyang and Wang, Xin and Hu, Shu},
    title     = {AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3503-3515}
}

raw_affiliation:
[{"affiliation": "Purdue University", "city": "West Lafayette", "country": "USA"}, {"affiliation": "University at Albany, State University of New York", "city": "New York", "country": "USA"}]


title: Inference-Scale Complexity in ANN-SNN Conversion for High-Performance and Low-Power Applications
authors: Tong Bu, Maohua Li, Zhaofei Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24387-24397
affiliations: ["Peking University", "Hohai University"]
cities: ["Beijing", "Nanjing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bu_Inference-Scale_Complexity_in_ANN-SNN_Conversion_for_High-Performance_and_Low-Power_Applications_CVPR_2025_paper.html

abstract:
Spiking Neural Networks (SNNs) have emerged as a promising substitute for Artificial Neural Networks (ANNs) due to their advantages of fast inference and low power consumption. However, the lack of efficient training algorithms has hindered their widespread adoption. Even efficient ANN-SNN conversion methods necessitate quantized training of ANNs to enhance the effectiveness of the conversion, incurring additional training costs. To address these challenges, we propose an efficient ANN-SNN conversion framework with only inference scale complexity. The conversion framework includes a local threshold balancing algorithm, which enables efficient calculation of the optimal thresholds and fine-grained adjustment of the threshold value by channel-wise scaling. We also introduce an effective delayed evaluation strategy to mitigate the influence of the spike propagation delays. We demonstrate the scalability of our framework in typical computer vision tasks: image classification, semantic segmentation, object detection, and video classification. Our algorithm outperforms existing methods, highlighting its practical applicability and efficiency. Moreover, we have evaluated the energy consumption of the converted SNNs, demonstrating their superior low-power advantage compared to conventional ANNs. This approach simplifies the deployment of SNNs by leveraging open-source pre-trained ANN models, enabling fast, low-power inference with negligible performance reduction.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bu_Inference-Scale_Complexity_in_ANN-SNN_Conversion_for_High-Performance_and_Low-Power_Applications_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bu_Inference-Scale_Complexity_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bu_2025_CVPR,
    author    = {Bu, Tong and Li, Maohua and Yu, Zhaofei},
    title     = {Inference-Scale Complexity in ANN-SNN Conversion for High-Performance and Low-Power Applications},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24387-24397}
}

raw_affiliation:
[{"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Hohai University", "city": "Nanjing", "country": "China"}]


title: Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12966-12977
cities: ["Unknown", "Hong Kong", "Beijing", "Shanghai"]
countries: ["Unknown", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Janus_Decoupling_Visual_Encoding_for_Unified_Multimodal_Understanding_and_Generation_CVPR_2025_paper.html

authors:
Chengyue Wu, Xiaokang Chen, Zhiyu Wu, Yiyang Ma, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, Chong Ruan, Ping Luo

abstract:
We introduce Janus, an autoregressive framework that unifies multimodal understanding and generation. Prior research often relies on a single visual encoder for both tasks, such as Chameleon. However, due to the differing levels of information granularity required by multimodal understanding and generation, this approach can lead to suboptimal performance, particularly in multimodal understanding. To address this issue, we decouple visual encoding into separate pathways, while still leveraging a single, unified transformer architecture for processing. The decoupling not only alleviates the conflict between the visual encoder's roles in understanding and generation, but also enhances the framework's flexibility. For instance, both the multimodal understanding and generation components can independently select their most suitable encoding methods. Experiments show that Janus surpasses previous unified model and matches or exceeds the performance of task-specific models. The simplicity, high flexibility, and effectiveness of Janus make it a strong candidate for next-generation unified multimodal models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Janus_Decoupling_Visual_Encoding_for_Unified_Multimodal_Understanding_and_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Janus_Decoupling_Visual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Chengyue and Chen, Xiaokang and Wu, Zhiyu and Ma, Yiyang and Liu, Xingchao and Pan, Zizheng and Liu, Wen and Xie, Zhenda and Yu, Xingkai and Ruan, Chong and Luo, Ping},
    title     = {Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12966-12977}
}

raw_affiliation:
[{"affiliation": "DeepSeek-AI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "HKU Shanghai Intelligent Computing Research Center", "city": "Shanghai", "country": "China"}]

affiliations:
["DeepSeek-AI", "The University of Hong Kong", "Peking University", "HKU Shanghai Intelligent Computing Research Center"]


authors: Jaeho Choi, Soheil Hor, Shubo Yang, Amin Arbabian
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27750-27759
affiliations: ["DGIST", "Stanford University"]
cities: ["Daegu", "Stanford"]
countries: ["South Korea", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Choi_MVDoppler-Pose_Multi-Modal_Multi-View_mmWave_Sensing_for_Long-Distance_Self-Occluded_Human_Walking_CVPR_2025_paper.html

title:
MVDoppler-Pose: Multi-Modal Multi-View mmWave Sensing for Long-Distance Self-Occluded Human Walking Pose Estimation

abstract:
One of the main challenges in reliable camera-based 3D pose estimation for walking subjects is to deal with self-occlusions, especially in the case of using low-resolution cameras or at longer distance scenarios. In recent years, millimeter-wave (mmWave) radar has emerged as a promising alternative, offering inherent resilience to the effect of occlusions and distance variations. However, mmWave-based human walking pose estimation (HWPE) is still in the nascent development stages, primarily due to its unique set of practical challenges including the quality of the observed radar signal dependent on the subject's motion direction. This paper introduces the first comprehensive study comparing mmWave radar to camera systems for HWPE, highlighting its utility for distance-agnostic and occlusion-resilient pose estimation. Building upon mmWave's unique advantages, we address its intrinsic directionality issue through a new approach--the synergetic integration of multi-modal, multi-view mmWave signals, achieving robust HWPE against variations both in distance and walking direction. Extensive experiments on a newly curated dataset not only demonstrate the superior potential of mmWave technology over traditional camera-based HWPE systems, but also validate the effectiveness of our approach in overcoming the core limitations of mmWave HWPE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Choi_MVDoppler-Pose_Multi-Modal_Multi-View_mmWave_Sensing_for_Long-Distance_Self-Occluded_Human_Walking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Choi_MVDoppler-Pose_Multi-Modal_Multi-View_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Choi_2025_CVPR,
    author    = {Choi, Jaeho and Hor, Soheil and Yang, Shubo and Arbabian, Amin},
    title     = {MVDoppler-Pose: Multi-Modal Multi-View mmWave Sensing for Long-Distance Self-Occluded Human Walking Pose Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27750-27759}
}

raw_affiliation:
[{"affiliation": "DGIST", "city": "Daegu", "country": "South Korea"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}]


authors: Xinjie Wang, Yifan Zhang, Ting Liu, Xinpu Liu, Ke Xu, Jianwei Wan, Yulan Guo, Hanyun Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27305-27314
cities: ["Changsha", "Beijing", "Guangzhou", "Zhengzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_TopNet_Transformer-Efficient_Occupancy_Prediction_Network_for_Octree-Structured_Point_Cloud_Geometry_CVPR_2025_paper.html

title:
TopNet: Transformer-Efficient Occupancy Prediction Network for Octree-Structured Point Cloud Geometry Compression

abstract:
Efficient Point Cloud Geometry Compression (PCGC) with a lower bits per point (BPP) and higher peak signal-to-noise ratio (PSNR) is essential for the transportation of large-scale 3D data. Although octree-based entropy models can reduce BPP without introducing geometry distortion, existing CNN-based models struggle with limited receptive fields to capture long-range dependencies, while Transformer-built architectures always neglect fine-grained details due to their reliance on global self-attention. In this paper, we propose a Transformer-efficient occupancy prediction Network, termed TopNet, to overcome these challenges by developing several novel components: Locally-enhanced Context Encoding (LeCE) for enhancing the translation-invariance of the octree nodes, Adaptive-Length Sliding Window Attention (AL-SWA) for capturing both global and local dependencies while adaptively adjusting attention weights based on the input window length, Spatial-Gated-enhanced Channel Mixer (SG-CM) for efficient feature aggregation from ancestors and siblings, and Latent-guided Node Occupancy Predictor (LNOP) for improving prediction accuracy of spatially adjacent octree nodes. Comprehensive experiments across both indoor and outdoor point cloud datasets demonstrate that our TopNet achieves state-of-the-art performance with fewer parameters, further advancing the reduction-efficiency boundaries of PCGC. The code is available at https://github.com/xinjiewang1995/TopNet.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_TopNet_Transformer-Efficient_Occupancy_Prediction_Network_for_Octree-Structured_Point_Cloud_Geometry_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_TopNet_Transformer-Efficient_Occupancy_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Xinjie and Zhang, Yifan and Liu, Ting and Liu, Xinpu and Xu, Ke and Wan, Jianwei and Guo, Yulan and Wang, Hanyun},
    title     = {TopNet: Transformer-Efficient Occupancy Prediction Network for Octree-Structured Point Cloud Geometry Compression},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27305-27314}
}

raw_affiliation:
[{"affiliation": "National University of Defense Technology", "city": "Changsha", "country": "China"}, {"affiliation": "Academy of Millitary Science", "city": "Beijing", "country": "China"}, {"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Information Engineering University", "city": "Zhengzhou", "country": "China"}]

affiliations:
["National University of Defense Technology", "Academy of Millitary Science", "Sun Yat-sen University", "Information Engineering University"]


title: MagicArticulate: Make Your 3D Models Articulation-Ready
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15998-16007
affiliations: ["Nanyang Technological University", "ByteDance Seed", "Institute for Inforcomm Research, A*ST AR"]
cities: ["Singapore", "Beijing", "Singapore"]
countries: ["Singapore", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Song_MagicArticulate_Make_Your_3D_Models_Articulation-Ready_CVPR_2025_paper.html

authors:
Chaoyue Song, Jianfeng Zhang, Xiu Li, Fan Yang, Yiwen Chen, Zhongcong Xu, Jun Hao Liew, Xiaoyang Guo, Fayao Liu, Jiashi Feng, Guosheng Lin

abstract:
With the explosive growth of 3D content creation, there is an increasing demand for automatically converting static 3D models into articulation-ready versions that support realistic animation. Traditional approaches rely heavily on manual annotation, which is both time-consuming and labor-intensive. Moreover, the lack of large-scale benchmarks has hindered the development of learning-based solutions. In this work, we present MagicArticulate, an effective framework that automatically transforms static 3D models into articulation-ready assets. Our key contributions are threefold. First, we introduce Articulation-XL, a large-scale benchmark containing over 33k 3D models with high-quality articulation annotations, carefully curated from Objaverse-XL. Second, we propose a novel skeleton generation method that formulates the task as a sequence modeling problem, leveraging an auto-regressive transformer to naturally handle varying numbers of bones or joints within skeletons and their inherent dependencies across different 3D models. Third, we predict skinning weights using a functional diffusion process that incorporates volumetric geodesic distance priors between vertices and joints. Extensive experiments demonstrate that MagicArticulate significantly outperforms existing methods across diverse object categories, achieving high-quality articulation that enables realistic animation. Project page: https://chaoyuesong.github.io/MagicArticulate.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Song_MagicArticulate_Make_Your_3D_Models_Articulation-Ready_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Song_MagicArticulate_Make_Your_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Song_2025_CVPR,
    author    = {Song, Chaoyue and Zhang, Jianfeng and Li, Xiu and Yang, Fan and Chen, Yiwen and Xu, Zhongcong and Liew, Jun Hao and Guo, Xiaoyang and Liu, Fayao and Feng, Jiashi and Lin, Guosheng},
    title     = {MagicArticulate: Make Your 3D Models Articulation-Ready},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15998-16007}
}

raw_affiliation:
[{"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "ByteDance Seed", "city": "Beijing", "country": "China"}, {"affiliation": "Institute for Inforcomm Research, A*ST AR", "city": "Singapore", "country": "Singapore"}]


authors: Zhou Yang, Mingtao Feng, Tao Huang, Fangfang Wu, Weisheng Dong, Xin Li, Guangming Shi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25497-25507
affiliations: ["Xidian University", "University at Albany, State University of New York"]
cities: ["Xi'an", "Albany"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Gain_from_Neighbors_Boosting_Model_Robustness_in_the_Wild_via_CVPR_2025_paper.html

title:
Gain from Neighbors: Boosting Model Robustness in the Wild via Adversarial Perturbations Toward Neighboring Classes

abstract:
Recent approaches, such as data augmentation, adversarial training, and transfer learning, have shown potential in addressing the issue of performance degradation caused by distributional shifts. However, they typically demand careful design in terms of data or models and lack awareness of the impact of distributional shifts. In this paper, we observe that classification errors arising from distribution shifts tend to cluster near the true values, suggesting that misclassifications commonly occur in semantically similar, neighboring categories. Furthermore, robust advanced vision foundation models maintain larger inter-class distances while preserving semantic consistency, making them less vulnerable to such shifts. Building on these findings, we propose a new method called GFN (Gain From Neighbors), which uses gradient priors from neighboring classes to perturb input images and incorporates an inter-class distance-weighted loss to improve class separation. This approach encourages the model to learn more resilient features from data prone to errors, enhancing its robustness against shifts in diverse settings. In extensive experiments across various model architectures and benchmark datasets, GFN consistently demonstrated superior performance. For instance, compared to the current state-of-the-art TAPADL method, our approach achieved a higher corruption robustness of 41.4% on ImageNet-C (+2.3%), without requiring additional parameters and using only minimal data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Gain_from_Neighbors_Boosting_Model_Robustness_in_the_Wild_via_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Gain_from_Neighbors_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Zhou and Feng, Mingtao and Huang, Tao and Wu, Fangfang and Dong, Weisheng and Li, Xin and Shi, Guangming},
    title     = {Gain from Neighbors: Boosting Model Robustness in the Wild via Adversarial Perturbations Toward Neighboring Classes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25497-25507}
}

raw_affiliation:
[{"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "University at Albany, State University of New York", "city": "Albany", "country": "USA"}]


title: Enhancing Video-LLM Reasoning via Agent-of-Thoughts Distillation
authors: Yudi Shi, Shangzhe Di, Qirui Chen, Weidi Xie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8523-8533
cities: ["Shanghai", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shi_Enhancing_Video-LLM_Reasoning_via_Agent-of-Thoughts_Distillation_CVPR_2025_paper.html

abstract:
This paper tackles the problem of video question answering (VideoQA), a task that often requires multi-step reasoning and a profound understanding of spatial-temporal dynamics. While large video-language models perform well on benchmarks, they often lack explainability and spatial-temporal grounding. In this paper, we propose **A**gent-**o**f-**T**houghts **D**istillation (**AoTD**), a method that enhances models by incorporating automatically generated Chain-of-Thoughts (CoTs) into the instruction-tuning process. Specifically, we leverage an agent-based system to decompose complex questions into sub-tasks, and address them with specialized vision models, the intermediate results are then treated as reasoning chains. We also introduce a verification mechanism using a large language model (LLM) to ensure the reliability of generated CoTs. Extensive experiments demonstrate that AoTD improves the performance on multiple-choice and open-ended benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shi_Enhancing_Video-LLM_Reasoning_via_Agent-of-Thoughts_Distillation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shi_Enhancing_Video-LLM_Reasoning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shi_2025_CVPR,
    author    = {Shi, Yudi and Di, Shangzhe and Chen, Qirui and Xie, Weidi},
    title     = {Enhancing Video-LLM Reasoning via Agent-of-Thoughts Distillation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8523-8533}
}

raw_affiliation:
[{"affiliation": "School of Arti\ufb01cial Intelligence, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Coop. Medianet Innovation Center, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["School of Arti\ufb01cial Intelligence, Shanghai Jiao Tong University", "Coop. Medianet Innovation Center, Shanghai Jiao Tong University"]


title: De^2Gaze: Deformable and Decoupled Representation Learning for 3D Gaze Estimation
authors: Yunfeng Xiao, Xiaowei Bai, Baojun Chen, Hao Su, Hao He, Liang Xie, Erwei Yin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3091-3100
cities: ["Tianjin", "Beijing", "Zhengzhou", "Tianjin"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_De2Gaze_Deformable_and_Decoupled_Representation_Learning_for_3D_Gaze_Estimation_CVPR_2025_paper.html

abstract:
3D Gaze estimation is a challenging task due to two main issues. First, existing methods focus on analyzing dense features (e.g., large pixel regions), which are sensitive to local noise (e.g., light spots, blurs) and result in increased computational complexity. Second, an eyeball model can correspond multiple gaze directions, and the entangled representation between gazes and models increases the learning difficulty. To address these issues, we propose De\textsuperscript 2Gaze , a lightweight and accurate model-aware 3D gaze estimation method. In De\textsuperscript 2 Gaze, we introduce two key innovations for deformable and decoupled representation learning. Specifically, first, we propose a deformable sparse attention mechanism that can adapt sparse sampling points to attention areas to avoid local noise influences. Second, we propose a spatial decoupling network with a dual-branch decoding architecture to disentangle invariant (e.g., eyeball radius, position) and variable (e.g., gaze, pupil, iris) features from the latent space. Compared to existing methods, De\textsuperscript 2 Gaze requires fewer sparse features, and achieves faster convergence speed, lower computational complexity, and higher accuracy in 3D gaze estimation.Qualitative and quantitative experiments demonstrate that De\textsuperscript 2 Gaze achieves state-of-the-art accuracy and high-quality semantic segmentation for 3D gaze estimation on the TEyeD dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_De2Gaze_Deformable_and_Decoupled_Representation_Learning_for_3D_Gaze_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiao_De2Gaze_Deformable_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xiao_2025_CVPR,
    author    = {Xiao, Yunfeng and Bai, Xiaowei and Chen, Baojun and Su, Hao and He, Hao and Xie, Liang and Yin, Erwei},
    title     = {De{\textasciicircum}2Gaze: Deformable and Decoupled Representation Learning for 3D Gaze Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3091-3100}
}

raw_affiliation:
[{"affiliation": "Tianjin University, China", "city": "Tianjin", "country": "China"}, {"affiliation": "Academy of Military Sciences (AMS), China", "city": "Beijing", "country": "China"}, {"affiliation": "Zhengzhou University, China", "city": "Zhengzhou", "country": "China"}, {"affiliation": "Tianjin Artificial Intelligence Innovation Center, China", "city": "Tianjin", "country": "China"}]

affiliations:
["Tianjin University, China", "Academy of Military Sciences (AMS), China", "Zhengzhou University, China", "Tianjin Artificial Intelligence Innovation Center, China"]


title: ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2050-2062
affiliations: ["Google", "Show Lab, National University of Singapore"]
cities: ["Mountain View", "Singapore"]
countries: ["USA", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_ReCapture_Generative_Video_Camera_Controls_for_User-Provided_Videos_using_Masked_CVPR_2025_paper.html

authors:
David Junhao Zhang, Roni Paiss, Shiran Zada, Nikhil Karnad, David E. Jacobs, Yael Pritch, Inbar Mosseri, Mike Zheng Shou, Neal Wadhwa, Nataniel Ruiz

abstract:
Recently, breakthroughs in video modeling have allowed for controllable camera trajectories in generated videos. However, these methods cannot be directly applied to user-provided videos that are not generated by a video model. In this paper, we present ReCapture, a method for generating new videos with novel camera trajectories from a single user-provided video.  Our method allows us to re-generate the reference video, with all its existing scene motion, from vastly different angles and with cinematic camera motion. Notably, using our method we can also plausibly hallucinate parts of the scene that were not observable in the reference video. Our method works by (1) generating a noisy anchor video with a new camera trajectory using multiview diffusion models or depth-based point cloud rendering and then (2) regenerating the anchor video into a clean and temporally consistent reangled video using our proposed masked video fine-tuning technique.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_ReCapture_Generative_Video_Camera_Controls_for_User-Provided_Videos_using_Masked_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_ReCapture_Generative_Video_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, David Junhao and Paiss, Roni and Zada, Shiran and Karnad, Nikhil and Jacobs, David E. and Pritch, Yael and Mosseri, Inbar and Shou, Mike Zheng and Wadhwa, Neal and Ruiz, Nataniel},
    title     = {ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2050-2062}
}

raw_affiliation:
[{"affiliation": "Google", "city": "Mountain View", "country": "USA"}, {"affiliation": "Show Lab, National University of Singapore", "city": "Singapore", "country": "Singapore"}]


title: M^3-VOS: Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation
authors: Zixuan Chen, Jiaxin Li, Junxuan Liang, Liming Tan, Yejie Guo, Cewu Lu, Yong-Lu Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29193-29202
affiliations: ["Shanghai Jiao Tong University", "Shanghai Innovation Institute"]
cities: ["Shanghai", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_M3-VOS_Multi-Phase_Multi-Transition_and_Multi-Scenery_Video_Object_Segmentation_CVPR_2025_paper.html

abstract:
Intelligent robots need to interact with diverse objects across various environments. The appearance and state of objects frequently undergo complex transformations depending on the object properties, e.g., phase transitions. However, in the vision community, segmenting dynamic objects with phase transitions is overlooked. In light of this, we introduce the concept of phase in segmentation, which categorizes real-world objects based on their visual characteristics and potential morphological and appearance changes. Then, we present a new benchmark, Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation (M^3-VOS), to verify the ability of models to understand object phases, which consists of 471 high-resolution videos spanning over 10 distinct everyday scenarios. It provides dense instance mask annotations that capture both object phases and their transitions. We evaluate state-of-the-art methods on M^3-VOS, yielding several key insights. Notably, current appearance-based approaches show significant room for improvement when handling objects with phase transitions. The inherent changes in disorder suggest that the predictive performance of the forward entropy-increasing process can be improved through a reverse entropy-reducing process. These findings lead us to propose ReVOS, a new plug-and-play model that improves its performance by reversal refinement. Our data and code will be publicly available at https://zixuan-chen.github.io/M-cube-VOS.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_M3-VOS_Multi-Phase_Multi-Transition_and_Multi-Scenery_Video_Object_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_M3-VOS_Multi-Phase_Multi-Transition_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Zixuan and Li, Jiaxin and Liang, Junxuan and Tan, Liming and Guo, Yejie and Lu, Cewu and Li, Yong-Lu},
    title     = {M{\textasciicircum}3-VOS: Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29193-29202}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Innovation Institute", "city": "Shanghai", "country": "China"}]


title: Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning
authors: Huiyi Wang, Haodong Lu, Lina Yao, Dong Gong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10087-10098
affiliations: ["University of New South Wales", "CSIRO\u2019s Data61"]
cities: ["Sydney", "Sydney"]
countries: ["Australia", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Self-Expansion_of_Pre-trained_Models_with_Mixture_of_Adapters_for_Continual_CVPR_2025_paper.html

abstract:
Continual learning (CL) aims to continually accumulate knowledge from a non-stationary data stream without catastrophic forgetting of learned knowledge, requiring a balance between stability and adaptability. Relying on the generalizable representation in pre-trained models (PTMs), PTM-based CL methods perform effective continual adaptation on downstream tasks by adding learnable adapters or prompts upon the frozen PTMs. However, many existing PTM-based CL methods use restricted adaptation on a fixed set of these modules to avoid forgetting, suffering from limited CL ability. Periodically adding task-specific modules results in linear model growth rate and impaired knowledge reuse. We propose Self-Expansion of pre-trained models with Modularized Adaptation (SEMA), a novel approach to enhance the control of stability-plasticity balance in PTM-based CL. SEMA automatically decides to reuse or add adapter modules on demand in CL, depending on whether significant distribution shift that cannot be handled is detected at different representation levels. We design modular adapter consisting of a functional adapter and a representation descriptor. The representation descriptors are trained as a distribution shift indicator and used to trigger self-expansion signals. For better composing the adapters, an expandable weighting router is learned jointly for mixture of adapter outputs. SEMA enables better knowledge reuse and sub-linear expansion rate. Extensive experiments demonstrate the effectiveness of the proposed self-expansion method, achieving state-of-the-art performance compared to PTM-based CL methods without memory rehearsal. Code is available at https://github.com/huiyiwang01/SEMA-CL.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Self-Expansion_of_Pre-trained_Models_with_Mixture_of_Adapters_for_Continual_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Self-Expansion_of_Pre-trained_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Huiyi and Lu, Haodong and Yao, Lina and Gong, Dong},
    title     = {Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10087-10098}
}

raw_affiliation:
[{"affiliation": "University of New South Wales", "city": "Sydney", "country": "Australia"}, {"affiliation": "CSIRO\u2019s Data61", "city": "Sydney", "country": "Australia"}]


title: Dual Prompting Image Restoration with Diffusion Transformers
authors: Dehong Kong, Fan Li, Zhixin Wang, Jiaqi Xu, Renjing Pei, Wenbo Li, WenQi Ren
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12809-12819
cities: ["Shenzhen", "Shenzhen", "Shenzhen", "Hong Kong", "Shenzhen"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kong_Dual_Prompting_Image_Restoration_with_Diffusion_Transformers_CVPR_2025_paper.html

abstract:
Recent state-of-the-art image restoration methods mostly adopt latent diffusion models with U-Net backbones, yet still facing challenges in achieving high-quality restoration due to their limited capabilities. Diffusion transformers (DiTs), like SD3, are emerging as a promising alternative because of their better quality with scalability. However, previous conditional control methods for U-Net-based diffusion models, such as ControlNet, are not well-suited for DiTs. In this paper, we introduce DPIR (Dual Recent state-of-the-art image restoration methods mostly adopt latent diffusion models with U-Net backbones, yet still facing challenges in achieving high-quality restoration due to their limited capabilities. Diffusion transformers (DiTs), like SD3, are emerging as a promising alternative because of their better quality with scalability. In this paper, we introduce DPIR (Dual Prompting Image Restoration), a novel image restoration method that effectivly extracts conditional information of low-quality images from multiple perspectives. Specifically, DPIR consits of two branches: a low-quality image conditioning branch and a dual prompting control branch. The first branch utilizes a lightweight module to incorporate image priors into the DiT with high efficiency. More importantly, we believe that in image restoration, textual description alone cannot fully capture its rich visual characteristics. Therefore, a dual prompting module is designed to provide DiT with additional visual cues, capturing both global context and local appearance. The extracted global-local visual prompts as extra conditional control, alongside textual prompts to form dual prompts, greatly enhance the quality of the restoration. Extensive experimental results demonstrate that DPIR delivers superior image restoration performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kong_Dual_Prompting_Image_Restoration_with_Diffusion_Transformers_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Kong_2025_CVPR,
    author    = {Kong, Dehong and Li, Fan and Wang, Zhixin and Xu, Jiaqi and Pei, Renjing and Li, Wenbo and Ren, WenQi},
    title     = {Dual Prompting Image Restoration with Diffusion Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12809-12819}
}

raw_affiliation:
[{"affiliation": "School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "MoE Key Laboratory of Information Technology", "city": "Shenzhen", "country": "China"}, {"affiliation": "Huawei Noah\u2019s Ark Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Guangdong Provincial Key Laboratory of Information Security Technology", "city": "Shenzhen", "country": "China"}]

affiliations:
["School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University", "MoE Key Laboratory of Information Technology", "Huawei Noah\u2019s Ark Lab", "The Chinese University of Hong Kong", "Guangdong Provincial Key Laboratory of Information Security Technology"]


title: Brain-Inspired Spiking Neural Networks for Energy-Efficient Object Detection
authors: Ziqi Li, Tao Gao, Yisheng An, Ting Chen, Jing Zhang, Yuanbo Wen, Mengkun Liu, Qianxi Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3552-3562
affiliations: ["Chang\u2019an University1", "Australian National University2"]
cities: ["Xi'an", "Canberra"]
countries: ["China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Brain-Inspired_Spiking_Neural_Networks_for_Energy-Efficient_Object_Detection_CVPR_2025_paper.html

abstract:
Brain-inspired spiking neural networks (SNNs) have the capability of energy-efficient processing of temporal information. However, leveraging the rich dynamic characteristics of SNNs and prior works in artificial neural networks (ANNs) to construct an effective object detection model for visual tasks remains an open question for further exploration. To develop a directly-trained , low energy consumption and high-performance multi-scale SNN model, we propose a novel interpretable object detection framework Multi-scale Spiking Detector (MSD). Initially, we propose a spiking convolutional neuron as a core component of the Optic Nerve Nucleus Block (ONNB), designed to significantly enhance the deep feature extraction capabilities of SNNs. ONNB enables direct training with improved energy efficiency, demonstrating superior performance compared to state-of-the-art ANN-to-SNN conversion and SNN techniques. In addition, we propose a Multi-scale Spiking Detection Framework to emulate the biological response and comprehension of stimuli from different objects. Wherein, spiking multi-scale fusion and the spiking detector are employed to integrate features across different depths and to detect response outcomes, respectively. Our method outperforms state-of-the-art ANN detectors, with only 7.8 M parameters and 6.43 mJ energy consumption. MSD obtains the mean average precision (mAP) of 62.0% and 66.3% on COCO and Gen1 datasets, respectively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Brain-Inspired_Spiking_Neural_Networks_for_Energy-Efficient_Object_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Brain-Inspired_Spiking_Neural_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Ziqi and Gao, Tao and An, Yisheng and Chen, Ting and Zhang, Jing and Wen, Yuanbo and Liu, Mengkun and Zhang, Qianxi},
    title     = {Brain-Inspired Spiking Neural Networks for Energy-Efficient Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3552-3562}
}

raw_affiliation:
[{"affiliation": "Chang\u2019an University1", "city": "Xi'an", "country": "China"}, {"affiliation": "Australian National University2", "city": "Canberra", "country": "Australia"}]


title: Medusa: A Multi-Scale High-order Contrastive Dual-Diffusion Approach for Multi-View Clustering
authors: Liang Chen, Zhe Xue, Yawen Li, Meiyu Liang, Yan Wang, Anton van den Hengel, Yuankai Qi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10295-10304
cities: ["Beijing", "Sydney", "Adelaide"]
countries: ["China", "Australia", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Medusa_A_Multi-Scale_High-order_Contrastive_Dual-Diffusion_Approach_for_Multi-View_Clustering_CVPR_2025_paper.html

abstract:
Deep multi-view clustering methods utilize information from multiple views to achieve enhanced clustering results and have gained increasing popularity in recent years. Most existing methods typically focus on either inter-view or intra-view relationships, aiming to align information across views or analyze structural patterns within individual views. However, they often incorporate inter-view complementary information in a simplistic manner, while overlooking the complex, high-order relationships within multi-view data and the interactions among samples, resulting in an incomplete utilization of the rich information available. Instead, we propose a multi-scale approach that exploits all of the available information. We first introduce a dual graph diffusion module guided by a consensus graph. This module leverages inter-view information to enhance the representation of both nodes and edges within each view. Secondly, we propose a novel contrastive loss function based on hypergraphs to more effectively model and leverage complex intra-view data relationships. Finally, we propose to adaptively learn fusion weights at the sample level, which enables a more flexible and dynamic aggregation of multi-view information. Extensive experiments on eight datasets show favorable performance of the proposed method compared to state-of-the-art approaches, demonstrating its effectiveness across diverse scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Medusa_A_Multi-Scale_High-order_Contrastive_Dual-Diffusion_Approach_for_Multi-View_Clustering_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Liang and Xue, Zhe and Li, Yawen and Liang, Meiyu and Wang, Yan and van den Hengel, Anton and Qi, Yuankai},
    title     = {Medusa: A Multi-Scale High-order Contrastive Dual-Diffusion Approach for Multi-View Clustering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10295-10304}
}

raw_affiliation:
[{"affiliation": "Beijing University of Posts and Telecommunications, China", "city": "Beijing", "country": "China"}, {"affiliation": "Macquarie University, Australia", "city": "Sydney", "country": "Australia"}, {"affiliation": "The University of Adelaide, Australia", "city": "Adelaide", "country": "Australia"}]

affiliations:
["Beijing University of Posts and Telecommunications, China", "Macquarie University, Australia", "The University of Adelaide, Australia"]


title: MambaOut: Do We Really Need Mamba for Vision?
authors: Weihao Yu, Xinchao Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4484-4496
raw_affiliation: [{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]
affiliations: ["National University of Singapore"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_MambaOut_Do_We_Really_Need_Mamba_for_Vision_CVPR_2025_paper.html

abstract:
Mamba, an architecture with RNN-like token mixer of state space model (SSM), was recently introduced to address the quadratic complexity of the attention mechanism and subsequently applied to vision tasks. Nevertheless, the performance of Mamba for vision is often underwhelming when compared with convolutional and attention-based models. In this paper, we delve into the essence of Mamba, and conceptually conclude that Mamba is ideally suited for tasks with long-sequence and autoregressive characteristics. For vision tasks, as image classification on ImageNet does not align with either characteristic, we hypothesize that Mamba is not necessary for this task; Detection and segmentation tasks on COCO or ADE20K are also not autoregressive, yet they adhere to the long-sequence characteristic, so we believe it is still worthwhile to explore Mamba's potential for these tasks. To empirically verify our hypotheses, we construct a series of models named MambaOut through stacking Mamba blocks while removing their core token mixer, SSM. Experimental results strongly support our hypotheses. Specifically, our MambaOut model surpasses all visual Mamba models on ImageNet image classification, indicating that Mamba is indeed unnecessary for this task. As for detection and segmentation, MambaOut cannot match the performance of state-of-the-art visual Mamba models, demonstrating the potential of Mamba for long-sequence visual tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_MambaOut_Do_We_Really_Need_Mamba_for_Vision_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_MambaOut_Do_We_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Weihao and Wang, Xinchao},
    title     = {MambaOut: Do We Really Need Mamba for Vision?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4484-4496}
}


title: Everything to the Synthetic: Diffusion-driven Test-time Adaptation via Synthetic-Domain Alignment
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30503-30513
affiliations: ["Tsinghua University", "SHI Labs @ Georgia Tech"]
cities: ["Beijing", "Atlanta"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Everything_to_the_Synthetic_Diffusion-driven_Test-time_Adaptation_via_Synthetic-Domain_Alignment_CVPR_2025_paper.html

authors:
Jiayi Guo, Junhao Zhao, Chaoqun Du, Yulin Wang, Chunjiang Ge, Zanlin Ni, Shiji Song, Humphrey Shi, Gao Huang

abstract:
Test-time adaptation (TTA) aims to improve the performance of source-domain pre-trained models on previously unseen, shifted target domains. Traditional TTA methods primarily adapt model weights based on target data streams, making model performance sensitive to the amount and order of target data. The recently proposed diffusion-driven TTA methods mitigate this by adapting model inputs instead of weights, where an unconditional diffusion model, trained on the source domain, transforms target-domain data into a synthetic domain that is expected to approximate the source domain. However, in this paper, we reveal that although the synthetic data in diffusion-driven TTA seems indistinguishable from the source data, it is unaligned with, or even markedly different from the latter for deep networks. To address this issue, we propose a Synthetic-Domain Alignment (SDA) framework. Our key insight is to fine-tune the source model with synthetic data to ensure better alignment. Specifically, we first employ a conditional diffusion model to generate labeled samples, creating a synthetic dataset. Subsequently, we use the aforementioned unconditional diffusion model to add noise to and denoise each sample before fine-tuning. This Mix of Diffusion (MoD) process mitigates the potential domain misalignment between the conditional and unconditional models. Extensive experiments across classifiers, segmenters, and multimodal large language models (MLLMs, e.g., LLaVA) demonstrate that SDA achieves superior domain alignment and consistently outperforms existing diffusion-driven TTA methods. Our code is available at https://github.com/SHI-Labs/Diffusion-Driven-Test-Time-Adaptation-via-Synthetic-Domain-Alignment.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Everything_to_the_Synthetic_Diffusion-driven_Test-time_Adaptation_via_Synthetic-Domain_Alignment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_Everything_to_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Jiayi and Zhao, Junhao and Du, Chaoqun and Wang, Yulin and Ge, Chunjiang and Ni, Zanlin and Song, Shiji and Shi, Humphrey and Huang, Gao},
    title     = {Everything to the Synthetic: Diffusion-driven Test-time Adaptation via Synthetic-Domain Alignment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30503-30513}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "SHI Labs @ Georgia Tech", "city": "Atlanta", "country": "USA"}]


authors: Peihua Deng, Jiehua Zhang, Xichun Sheng, Chenggang Yan, Yaoqi Sun, Ying Fu, Liang Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30566-30576
cities: ["Hangzhou", "Xi'an", "Macau", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Deng_Multi-Granularity_Class_Prototype_Topology_Distillation_for_Class-Incremental_Source-Free_Unsupervised_Domain_CVPR_2025_paper.html

title:
Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation

abstract:
This paper explores the Class-Incremental Source-Free Unsupervised Domain Adaptation (CI-SFUDA) problem, where the unlabeled target data come incrementally without access to labeled source instances. This problem poses two challenges, the interference of similar source-class knowledge in target-class representation learning and the shocks of new target knowledge to old ones. To address them, we propose the Multi-Granularity Class Prototype Topology Distillation (GROTO) algorithm, which effectively transfers the source knowledge to the class-incremental target domain. Concretely, we design the multi-granularity class prototype self-organization module and the prototype topology distillation module. First, we mine the positive classes by modeling accumulation distributions. Next, we introduce multi-granularity class prototypes to generate reliable pseudo-labels, and exploit them to promote the positive-class target feature self-organization. Second, the positive-class prototypes are leveraged to construct the topological structures of source and target feature spaces. Then, we perform the topology distillation to continually mitigate the shocks of new target knowledge to old ones. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance on three public datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Deng_Multi-Granularity_Class_Prototype_Topology_Distillation_for_Class-Incremental_Source-Free_Unsupervised_Domain_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Deng_Multi-Granularity_Class_Prototype_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Deng_2025_CVPR,
    author    = {Deng, Peihua and Zhang, Jiehua and Sheng, Xichun and Yan, Chenggang and Sun, Yaoqi and Fu, Ying and Li, Liang},
    title     = {Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30566-30576}
}

raw_affiliation:
[{"affiliation": "Hangzhou Dianzi University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Macao Polytechnic University", "city": "Macau", "country": "China"}, {"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "Institute of Computing Technology, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["Hangzhou Dianzi University", "Xi\u2019an Jiaotong University", "Macao Polytechnic University", "Beijing Institute of Technology", "Institute of Computing Technology, Chinese Academy of Sciences"]


title: DepthCues: Evaluating Monocular Depth Perception in Large Vision Models
authors: Duolikun Danier, Mehmet Aygün, Changjian Li, Hakan Bilen, Oisin Mac Aodha
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20049-20059
raw_affiliation: [{"affiliation": "University of Edinburgh", "city": "Edinburgh", "country": "UK"}]
affiliations: ["University of Edinburgh"]
cities: ["Edinburgh"]
countries: ["UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Danier_DepthCues_Evaluating_Monocular_Depth_Perception_in_Large_Vision_Models_CVPR_2025_paper.html

abstract:
Large-scale pre-trained vision models are becoming increasingly prevalent, offering expressive and generalizable visual representations that benefit various downstream tasks. Recent studies on the emergent properties of these models have revealed their high-level geometric understanding, in particular in the context of depth perception. However, it remains unclear how depth perception arises in these models without explicit depth supervision provided during pre-training. To investigate this, we examine whether the monocular depth cues, similar to those used by the human visual system, emerge in these models. We introduce a new benchmark, DepthCues, designed to evaluate depth cue understanding, and present findings across 20 diverse and representative pre-trained vision models. Our analysis shows that human-like depth cues emerge in more recent larger models. We also explore enhancing depth perception in large vision models by fine-tuning on DepthCues, and find that even without dense depth supervision, this improves depth estimation. To support further research, our benchmark and evaluation code will be made publicly available for studying depth perception in vision models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Danier_DepthCues_Evaluating_Monocular_Depth_Perception_in_Large_Vision_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Danier_DepthCues_Evaluating_Monocular_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Danier_2025_CVPR,
    author    = {Danier, Duolikun and Ayg\"un, Mehmet and Li, Changjian and Bilen, Hakan and Mac Aodha, Oisin},
    title     = {DepthCues: Evaluating Monocular Depth Perception in Large Vision Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20049-20059}
}


title: A Polarization-Aided Transformer for Image Deblurring via Motion Vector Decomposition
authors: Duosheng Chen, Shihao Zhou, Jinshan Pan, Jinglei Shi, Lishen Qu, Jufeng Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28061-28070
cities: ["Shenzhen", "Tianjin", "Nanjing"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_A_Polarization-Aided_Transformer_for_Image_Deblurring_via_Motion_Vector_Decomposition_CVPR_2025_paper.html

abstract:
Effectively leveraging motion information is crucial for the image deblurring task. Existing methods typically build deep-learning models to restore a clean image by estimating blur patterns over the entire movement. This suggests that the blur caused by rotational motion components is processed together with the translational one. Exploring the movement without separation leads to limited performance for complex motion deblurring, especially rotational motion. In this paper, we propose Motion Decomposition Transformer (MDT), a transformer-based architecture augmented with polarized modules for deblurring via motion vector decomposition. MDT consists of a Motion Decomposition Module (MDM) for extracting hybrid rotation and translation features, and a Radial Stripe Attention Solver (RSAS) for sharp image reconstruction with enhanced rotational information. Specifically, the MDM uses a deformable Cartesian convolutional branch to capture translational motion, complemented by a polar-system branch to capture rotational motion. The RSAS employs radial stripe windows and angular relative positional encoding in the polar system to enhance rotational information. This design preserves translational details while keeping computational costs lower than dual-coordinate design. Experimental results on 6 image deblurring datasets show that MDT outperforms state-of-the-art methods, particularly in handling blur caused by complex motions with significant rotational components.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_A_Polarization-Aided_Transformer_for_Image_Deblurring_via_Motion_Vector_Decomposition_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Duosheng and Zhou, Shihao and Pan, Jinshan and Shi, Jinglei and Qu, Lishen and Yang, Jufeng},
    title     = {A Polarization-Aided Transformer for Image Deblurring via Motion Vector Decomposition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28061-28070}
}

raw_affiliation:
[{"affiliation": "Nankai International Advanced Research Institute (SHENZHEN\u00b7 FUTIAN)", "city": "Shenzhen", "country": "China"}, {"affiliation": "Pengcheng Laboratory, 3 VCIP & TMCC & DISSec, College of Computer Science, Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "School of Computer Science and Engineering, Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}]

affiliations:
["Nankai International Advanced Research Institute (SHENZHEN\u00b7 FUTIAN)", "Pengcheng Laboratory, 3 VCIP & TMCC & DISSec, College of Computer Science, Nankai University", "School of Computer Science and Engineering, Nanjing University of Science and Technology"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16133-16142
cities: ["Beijing", "Beijing", "Unknown", "Harbin", "Hong Kong"]
countries: ["China", "China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_SpecTRe-GS_Modeling_Highly_Specular_Surfaces_with_Reflected_Nearby_Objects_by_CVPR_2025_paper.html

title:
SpecTRe-GS: Modeling Highly Specular Surfaces with Reflected Nearby Objects by Tracing Rays in 3D Gaussian Splatting

authors:
Jiajun Tang, Fan Fei, Zhihao Li, Xiao Tang, Shiyong Liu, Youyu Chen, Binxiao Huang, Zhenyu Chen, Xiaofei Wu, Boxin Shi

abstract:
3D Gaussian Splatting (3DGS), a recently emerged multi-view 3D reconstruction technique, has shown significant advantages in real-time rendering and explicit editing. However, 3DGS encounters challenges in the accurate modeling of both high-frequency view-dependent appearances and global illumination effects, including inter-reflection. This paper introduces SpecTRe-GS, which addresses these challenges and models highly Specular surfaces that reflect nearby objects through Tracing Rays in 3D Gaussian Splatting. SpecTRe-GS separately models reflections from highly specular and rough surfaces to leverage the distinctions between their reflective properties and integrates an efficient ray tracer within the 3DGS framework for querying secondary rays, thus achieving fast and accurate rendering. Also, it incorporates normal prior guidance and joint geometry optimization at various stages of the training process to enhance geometry reconstruction for undistorted reflections. Experiments on both synthetic and real-world scenes demonstrate the superiority of SpecTRe-GS compared to existing 3DGS-based methods in capturing highly specular inter-reflections and also showcase its editing applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_SpecTRe-GS_Modeling_Highly_Specular_Surfaces_with_Reflected_Nearby_Objects_by_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_SpecTRe-GS_Modeling_Highly_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Jiajun and Fei, Fan and Li, Zhihao and Tang, Xiao and Liu, Shiyong and Chen, Youyu and Huang, Binxiao and Chen, Zhenyu and Wu, Xiaofei and Shi, Boxin},
    title     = {SpecTRe-GS: Modeling Highly Specular Surfaces with Reflected Nearby Objects by Tracing Rays in 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16133-16142}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "National Engineering Research Center of Visual Technology, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Huawei Noah\u2019s Ark Lab", "city": "Unknown", "country": "China"}, {"affiliation": "Harbin Institute of Technology", "city": "Harbin", "country": "China"}, {"affiliation": "University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "National Engineering Research Center of Visual Technology, School of Computer Science, Peking University", "Huawei Noah\u2019s Ark Lab", "Harbin Institute of Technology", "University of Hong Kong"]


title: Seurat: From Moving Points to Depth
authors: Seokju Cho, Jiahui Huang, Seungryong Kim, Joon-Young Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7211-7221
affiliations: ["KAIST AI", "Adobe Research"]
cities: ["Daejeon", "San Jose"]
countries: ["South Korea", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cho_Seurat_From_Moving_Points_to_Depth_CVPR_2025_paper.html

abstract:
Accurate depth estimation from monocular videos remains challenging due to ambiguities inherent in single-view geometry, as crucial depth cues like stereopsis are absent. However, humans often perceive relative depth intuitively by observing variations in the size and spacing of objects as they move. Inspired by this, we propose a novel method that infers relative depth by examining the spatial relationships and temporal evolution of a set of tracked 2D trajectories. Specifically, we use off-the-shelf point tracking models to capture 2D trajectories. Then, our approach employs spatial and temporal transformers to process these trajectories and directly infer depth changes over time. Evaluated on the TAPVid-3D benchmark, our method demonstrates robust zero-shot performance, generalizing effectively from synthetic to real-world datasets. Results indicate that our approach achieves temporally smooth, high-accuracy depth predictions across diverse domains.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cho_Seurat_From_Moving_Points_to_Depth_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cho_Seurat_From_Moving_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cho_2025_CVPR,
    author    = {Cho, Seokju and Huang, Jiahui and Kim, Seungryong and Lee, Joon-Young},
    title     = {Seurat: From Moving Points to Depth},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7211-7221}
}

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


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16366-16376
affiliations: ["National Yang Ming Chiao Tung University", "NVIDIA"]
cities: ["Hsinchu", "Santa Clara"]
countries: ["Taiwan", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_AuraFusion360_Augmented_Unseen_Region_Alignment_for_Reference-based_360deg_Unbounded_Scene_CVPR_2025_paper.html

title:
AuraFusion360: Augmented Unseen Region Alignment for Reference-based 360deg Unbounded Scene Inpainting

authors:
Chung-Ho Wu, Yang-Jung Chen, Ying-Huan Chen, Jie-Ying Lee, Bo-Hsu Ke, Chun-Wei Tuan Mu, Yi-Chuan Huang, Chin-Yang Lin, Min-Hung Chen, Yen-Yu Lin, Yu-Lun Liu

abstract:
Three-dimensional scene inpainting is crucial for applications from virtual reality to architectural visualization, yet existing methods struggle with view consistency and geometric accuracy in 360deg unbounded scenes. We present AuraFusion360, a novel reference-based method that enables high-quality object removal and hole filling in 3D scenes represented by Gaussian Splatting. Our approach introduces (1) depth-aware unseen mask generation for accurate occlusion identification, (2) Adaptive Guided Depth Diffusion, a zero-shot method for accurate initial point placement without requiring additional training, and (3) SDEdit-based detail enhancement for multi-view coherence. We also introduce 360-USID, the first comprehensive dataset for 360deg unbounded scene inpainting with ground truth. Extensive experiments demonstrate that AuraFusion360 significantly outperforms existing methods, achieving superior perceptual quality while maintaining geometric accuracy across dramatic viewpoint changes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_AuraFusion360_Augmented_Unseen_Region_Alignment_for_Reference-based_360deg_Unbounded_Scene_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_AuraFusion360_Augmented_Unseen_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Chung-Ho and Chen, Yang-Jung and Chen, Ying-Huan and Lee, Jie-Ying and Ke, Bo-Hsu and Mu, Chun-Wei Tuan and Huang, Yi-Chuan and Lin, Chin-Yang and Chen, Min-Hung and Lin, Yen-Yu and Liu, Yu-Lun},
    title     = {AuraFusion360: Augmented Unseen Region Alignment for Reference-based 360deg Unbounded Scene Inpainting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16366-16376}
}

raw_affiliation:
[{"affiliation": "National Yang Ming Chiao Tung University", "city": "Hsinchu", "country": "Taiwan"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}]


title: Language-Guided Image Tokenization for Generation
authors: Kaiwen Zha, Lijun Yu, Alireza Fathi, David A. Ross, Cordelia Schmid, Dina Katabi, Xiuye Gu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15713-15722
affiliations: ["Google DeepMind", "MIT CSAIL"]
cities: ["Mountain View", "Cambridge"]
countries: ["USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zha_Language-Guided_Image_Tokenization_for_Generation_CVPR_2025_paper.html

abstract:
Image tokenization, the process of transforming raw image pixels into a compact low-dimensional latent representation, has proven crucial for scalable and efficient image generation. However, mainstream image tokenization methods generally have limited compression rates, making high-resolution image generation computationally expensive. To address this challenge, we propose to leverage language for efficient image tokenization, and we call our method Text-Conditioned Image Tokenization (TexTok). TexTok is a simple yet effective tokenization framework that leverages language to provide a compact, high-level semantic representation. By conditioning the tokenization process on descriptive text captions, TexTok simplifies semantic learning, allowing more learning capacity and token space to be allocated to capture fine-grained visual details, leading to enhanced reconstruction quality and higher compression rates. Compared to the conventional tokenizer without text conditioning, TexTok achieves average reconstruction FID improvements of 29.2% and 48.1% on ImageNet-256 and -512 benchmarks respectively, across varying numbers of tokens. These tokenization improvements consistently translate to 16.3% and 34.3% average improvements in generation FID. By simply replacing the tokenizer in Diffusion Transformer (DiT) with TexTok, our system can achieve a 93.5x inference speedup while still outperforming the original DiT using only 32 tokens on ImageNet-512. TexTok with a vanilla DiT generator achieves state-of-the-art FID scores of 1.46 and 1.62 on ImageNet-256 and -512 respectively. Furthermore, we demonstrate TexTok's superiority on the text-to-image generation task, effectively utilizing the off-the-shelf text captions in tokenization.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zha_Language-Guided_Image_Tokenization_for_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zha_Language-Guided_Image_Tokenization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zha_2025_CVPR,
    author    = {Zha, Kaiwen and Yu, Lijun and Fathi, Alireza and Ross, David A. and Schmid, Cordelia and Katabi, Dina and Gu, Xiuye},
    title     = {Language-Guided Image Tokenization for Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15713-15722}
}

raw_affiliation:
[{"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}, {"affiliation": "MIT CSAIL", "city": "Cambridge", "country": "USA"}]


title: Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models
authors: Qirui Jiao, Daoyuan Chen, Yilun Huang, Bolin Ding, Yaliang Li, Ying Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9296-9307
affiliations: ["Sun Yat-Sen University", "Alibaba Group", "FSIETP"]
cities: ["Guangzhou", "Hangzhou", "Guangdong"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiao_Img-Diff_Contrastive_Data_Synthesis_for_Multimodal_Large_Language_Models_CVPR_2025_paper.html

abstract:
High-performance Multimodal Large Language Models (MLLMs) rely heavily on data quality. This study introduces a novel data synthesis method, leveraging insights from contrastive learning and image difference captioning to enhance fine-grained image recognition in MLLMs. By analyzing object differences in detailed regions between similar images, we challenge the model to identify both matching and distinct components. Specifically, our method initially create pairs of similar images that highlight object variations. After that, we introduce a Difference Area Generator for object differences identifying, followed by a Difference Captions Generator for differences describing. The outcome is a high-quality dataset of "object replacement" samples, named Img-Diff, which can be expanded as needed due to its automation. We use the generated dataset to finetune state-of-the-art (SOTA) MLLMs such as InternVL2, yielding comprehensive improvements across numerous image difference and Visual Question Answering tasks. For instance, the trained models notably surpass the SOTA models GPT-4V and Gemini on the MMVP benchmark. Additionally, we conduct thorough evaluations to confirm the dataset's diversity, quality, and robustness, presenting several insights on the synthesis of such a contrastive dataset. We release our codes and dataset to encourage further research on multimodal data synthesis and MLLMs' fundamental capabilities for image understanding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiao_Img-Diff_Contrastive_Data_Synthesis_for_Multimodal_Large_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiao_Img-Diff_Contrastive_Data_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiao_2025_CVPR,
    author    = {Jiao, Qirui and Chen, Daoyuan and Huang, Yilun and Ding, Bolin and Li, Yaliang and Shen, Ying},
    title     = {Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9296-9307}
}

raw_affiliation:
[{"affiliation": "Sun Yat-Sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "FSIETP", "city": "Guangdong", "country": "China"}]


title: CocoER: Aligning Multi-Level Feature by Competition and Coordination for Emotion Recognition
authors: Xuli Shen, Hua Cai, Weilin Shen, Qing Xu, Dingding Yu, Weifeng Ge, Xiangyang Xue
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29591-29600
affiliations: ["Fudan University", "UniDT"]
cities: ["Shanghai", "Unknown"]
countries: ["China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shen_CocoER_Aligning_Multi-Level_Feature_by__Competition_and_Coordination_for_CVPR_2025_paper.html

abstract:
With the explosion of human-machine interaction, emotion recognition has  reignited attention. Previous works  focus on improving visual feature fusion and reasoning from multiple image levels.  Although it is non-trivial to deduce a person's emotion by  integrating multi-level feature (head, body and context), the emotion recognition results of each level is usually different from one another, which creates inconsistency in the prevailing feature alignment method and decrease recognition performance. In this work, we propose a multi-level image feature refinement method for emotion recognition (CocoER) to mitigate the impact caused by conflicting results from  multi-level recognition. First, we leverage cross-level  attention to improve visual feature consistency between hierarchically cropped head, body and context windows.  Then, vocabulary informed alignment is incorporated into the recognition framework to produce pseudo label and guide hierarchical visual feature refinement. To effectively fuse multi-level feature, we elaborate on a competition process of eliminating irrelevant  image level predictions and a coordination process to enhance the  feature across all levels. Extensive experiments are executed on two popular datasets, and our method achieves  state-of-the-art performance with  multi-level interpretation results.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_CocoER_Aligning_Multi-Level_Feature_by__Competition_and_Coordination_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shen_CocoER_Aligning_Multi-Level_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shen_2025_CVPR,
    author    = {Shen, Xuli and Cai, Hua and Shen, Weilin and Xu, Qing and Yu, Dingding and Ge, Weifeng and Xue, Xiangyang},
    title     = {CocoER: Aligning Multi-Level Feature by  Competition and Coordination for Emotion Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29591-29600}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "UniDT", "city": "Unknown", "country": "Unknown"}]


title: Hyperbolic Uncertainty-Aware Few-Shot Incremental Point Cloud Segmentation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11810-11821
cities: ["Chennai", "Mumbai", "Kharagpur", "Abu Dhabi"]
countries: ["India", "India", "India", "UAE"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sur_Hyperbolic_Uncertainty-Aware_Few-Shot_Incremental_Point_Cloud_Segmentation_CVPR_2025_paper.html

authors:
Tanuj Sur, Samrat Mukherjee, Kaizer Rahaman, Subhasis Chaudhuri, Muhammad Haris Khan, Biplab Banerjee

abstract:
3D point cloud segmentation is essential across a range of applications; however, conventional methods often struggle in evolving environments, particularly when tasked with identifying novel categories under limited supervision. Few-Shot Learning (FSL) and Class Incremental Learning (CIL) have been adapted previously to address these challenges in isolation, yet the combined paradigm of Few-Shot Class Incremental Learning (FSCIL) remains largely unexplored for point cloud segmentation. To address this gap, we introduce Hyperbolic Ideal Prototypes Optimization (HIPO), a novel framework that harnesses hyperbolic embeddings for FSCIL in 3D point clouds. HIPO employs the Poincare Hyperbolic Sphere as its embedding space, integrating Ideal Prototypes enriched by CLIP-derived class semantics, to capture the hierarchical structure of 3D data. By enforcing orthogonality among prototypes and maximizing representational margins, HIPO constructs a resilient embedding space that mitigates forgetting and enables the seamless integration of new classes, thereby effectively countering overfitting. Extensive evaluations on S3DIS, ScanNetv2, and cross-dataset scenarios demonstrate HIPO's strong performance, significantly surpassing existing approaches in both in-domain and cross-dataset FSCIL tasks for 3D point cloud segmentation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sur_Hyperbolic_Uncertainty-Aware_Few-Shot_Incremental_Point_Cloud_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sur_Hyperbolic_Uncertainty-Aware_Few-Shot_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sur_2025_CVPR,
    author    = {Sur, Tanuj and Mukherjee, Samrat and Rahaman, Kaizer and Chaudhuri, Subhasis and Khan, Muhammad Haris and Banerjee, Biplab},
    title     = {Hyperbolic Uncertainty-Aware Few-Shot Incremental Point Cloud Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11810-11821}
}

raw_affiliation:
[{"affiliation": "Chennai Mathematical Institute", "city": "Chennai", "country": "India"}, {"affiliation": "Indian Institute of Technology Bombay", "city": "Mumbai", "country": "India"}, {"affiliation": "Indian Institute of Technology Kharagpur", "city": "Kharagpur", "country": "India"}, {"affiliation": "Mohamed Bin Zayed University of Artificial Intelligence", "city": "Abu Dhabi", "country": "UAE"}]

affiliations:
["Chennai Mathematical Institute", "Indian Institute of Technology Bombay", "Indian Institute of Technology Kharagpur", "Mohamed Bin Zayed University of Artificial Intelligence"]


title: Enhancing Creative Generation on Stable Diffusion-based Models
authors: Jiyeon Han, Dahee Kwon, Gayoung Lee, Junho Kim, Jaesik Choi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28609-28618
affiliations: ["KAIST AI", "NA VER AI Lab", "INEEJI"]
cities: ["Daejeon", "Seongnam", "Daejeon"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Han_Enhancing_Creative_Generation_on_Stable_Diffusion-based_Models_CVPR_2025_paper.html

abstract:
Recent text-to-image generative models, particularly Stable Diffusion and its distilled variants, have achieved impressive fidelity and strong text-image alignment. However, their creative generation capacity remains limited, as simply adding the term "creative" to prompts often fails to yield genuinely creative results. In this paper, we introduce C3 (Creative Concept Catalyst), a training-free approach designed to enhance creativity in Stable Diffusion-based models. C3 selectively amplifies features during the denoising process to foster more creative outputs. We offer practical guidelines for choosing amplification factors based on two main aspects of creativity. C3 allows user-friendly creativity control in image generation and is the first study to enhance creativity in diffusion models without extensive computational costs. We demonstrate its effectiveness across various Stable Diffusion-based models. Source codes will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Han_Enhancing_Creative_Generation_on_Stable_Diffusion-based_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Han_Enhancing_Creative_Generation_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Han_2025_CVPR,
    author    = {Han, Jiyeon and Kwon, Dahee and Lee, Gayoung and Kim, Junho and Choi, Jaesik},
    title     = {Enhancing Creative Generation on Stable Diffusion-based Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28609-28618}
}

raw_affiliation:
[{"affiliation": "KAIST AI", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "NA VER AI Lab", "city": "Seongnam", "country": "South Korea"}, {"affiliation": "INEEJI", "city": "Daejeon", "country": "South Korea"}]


title: The Devil is in the Prompts: Retrieval-Augmented Prompt Optimization for Text-to-Video Generation
authors: Bingjie Gao, Xinyu Gao, Xiaoxue Wu, Yujie Zhou, Yu Qiao, Li Niu, Xinyuan Chen, Yaohui Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3173-3183
cities: ["Shanghai", "Shanghai", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_The_Devil_is_in_the_Prompts_Retrieval-Augmented_Prompt_Optimization_for_CVPR_2025_paper.html

abstract:
The evolution of Text-to-video (T2V) generative models, trained on large-scale datasets, has been marked by significant progress. However, the sensitivity of T2V generative models to input prompts highlights the critical role of prompt design in influencing generative outcomes. Prior research has predominantly relied on Large Language Models (LLMs) to align user-provided prompts with the distribution of training prompts, albeit without tailored guidance encompassing prompt vocabulary and sentence structure nuances. To this end, we introduce RAPO, a novel Retrieval-Augmented Prompt Optimization framework. In order to address potential inaccuracies and ambiguous details generated by LLM-generated prompts. RAPO refines the naive prompts through dual optimization branches, selecting the superior prompt for T2V generation. The first branch augments user prompts with diverse modifiers extracted from a learned relational graph, refining them to align with the format of training prompts via a fine-tuned LLM. Conversely, the second branch rewrites the naive prompt using a pre-trained LLM following a well-defined instruction set. Extensive experiments demonstrate that RAPO can effectively enhance both the static and dynamic dimensions of generated videos, demonstrating the significance of prompt optimization for user-provided prompts. Project website: \href https://whynothaha.github.io/Prompt_optimizer/RAPO.html  GitHub .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_The_Devil_is_in_the_Prompts_Retrieval-Augmented_Prompt_Optimization_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_The_Devil_is_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Bingjie and Gao, Xinyu and Wu, Xiaoxue and Zhou, Yujie and Qiao, Yu and Niu, Li and Chen, Xinyuan and Wang, Yaohui},
    title     = {The Devil is in the Prompts: Retrieval-Augmented Prompt Optimization for Text-to-Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3173-3183}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}]

affiliations:
["Shanghai Jiao Tong University", "Shanghai Artificial Intelligence Laboratory", "Fudan University"]


title: Denoising Functional Maps: Diffusion Models for Shape Correspondence
authors: Aleksei Zhuravlev, Zorah Lähner, Vladislav Golyanik
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26899-26909
affiliations: ["University of Bonn", "Lamarr Institute", "MPI for Informatics, SIC"]
cities: ["Bonn", "Bonn", "Saarbr\u00fccken"]
countries: ["Germany", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhuravlev_Denoising_Functional_Maps_Diffusion_Models_for_Shape_Correspondence_CVPR_2025_paper.html

abstract:
Estimating correspondences between pairs of deformable shapes remains a challenging problem. Despite substantial progress, existing methods lack broad generalization capabilities and require category-specific training data. To address these limitations, we propose a fundamentally new approach to shape correspondence based on denoising diffusion models. In our method, a diffusion model learns to directly predict the functional map, a low-dimensional representation of a point-wise map between shapes. We use a large dataset of synthetic human meshes for training and employ two steps to reduce the number of functional maps that need to be learned. First, the maps refer to a template rather than shape pairs. Second, the functional map is defined in a basis of eigenvectors of the Laplacian, which is not unique due to sign ambiguity. Therefore, we introduce an unsupervised approach to select a specific basis by correcting the signs of eigenvectors based on surface features. Our model achieves competitive performance on standard human datasets, meshes with anisotropic connectivity, non-isometric humanoid shapes, as well as animals compared to existing descriptor-based and large-scale shape deformation methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhuravlev_Denoising_Functional_Maps_Diffusion_Models_for_Shape_Correspondence_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhuravlev_Denoising_Functional_Maps_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhuravlev_2025_CVPR,
    author    = {Zhuravlev, Aleksei and L\"ahner, Zorah and Golyanik, Vladislav},
    title     = {Denoising Functional Maps: Diffusion Models for Shape Correspondence},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26899-26909}
}

raw_affiliation:
[{"affiliation": "University of Bonn", "city": "Bonn", "country": "Germany"}, {"affiliation": "Lamarr Institute", "city": "Bonn", "country": "Germany"}, {"affiliation": "MPI for Informatics, SIC", "city": "Saarbr\u00fccken", "country": "Germany"}]


title: ProReflow: Progressive Reflow with Decomposed Velocity
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28029-28038
cities: ["Beijing", "Shanghai", "Shenzhen", "Chengdu"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ke_ProReflow_Progressive_Reflow_with_Decomposed_Velocity_CVPR_2025_paper.html

authors:
Lei Ke, Haohang Xu, Xuefei Ning, Yu Li, Jiajun Li, Haoling Li, Yuxuan Lin, Dongsheng Jiang, Yujiu Yang, Linfeng Zhang

abstract:
Diffusion models have achieved significant progress in both image and video generation while still suffering from huge computation costs. As an effective solution, rectified flow aims to rectify the diffusion process of diffusion models into a straight line for few-step and even one-step generation. However, in this paper, we suggest that the original training pipeline of reflow is not optimal and introduce two techniques to improve it. Firstly, we introduce progressive reflow, which progressively reflows the diffusion models in local timesteps until the whole diffusion progresses, reducing the difficulty of flow matching. Second, we introduce aligned v-prediction, which highlights the importance of direction matching in flow matching over magnitude matching. Experimental results on SDv1.5 and SDXL demonstrate the effectiveness of our method, for example, conducting on SDv1.5 achieves an FID of 10.70 on MSCOCO2014 validation set with only 4 sampling steps, close to our teacher model (32 DDIM steps, FID = 10.05). Our codes will be released at Github.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ke_ProReflow_Progressive_Reflow_with_Decomposed_Velocity_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ke_ProReflow_Progressive_Reflow_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ke_2025_CVPR,
    author    = {Ke, Lei and Xu, Haohang and Ning, Xuefei and Li, Yu and Li, Jiajun and Li, Haoling and Lin, Yuxuan and Jiang, Dongsheng and Yang, Yujiu and Zhang, Linfeng},
    title     = {ProReflow: Progressive Reflow with Decomposed Velocity},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28029-28038}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Huawei Inc.", "city": "Shenzhen", "country": "China"}, {"affiliation": "University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}]

affiliations:
["Tsinghua University", "Shanghai Jiao Tong University", "Huawei Inc.", "University of Electronic Science and Technology of China"]


title: DnLUT: Ultra-Efficient Color Image Denoising via Channel-Aware Lookup Tables
authors: Sidi Yang, Binxiao Huang, Yulun Zhang, Dahai Yu, Yujiu Yang, Ngai Wong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7582-7591
cities: ["Hong Kong", "Shanghai", "Shenzhen", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_DnLUT_Ultra-Efficient_Color_Image_Denoising_via_Channel-Aware_Lookup_Tables_CVPR_2025_paper.html

abstract:
While deep neural networks have revolutionized image denoising capabilities, their deployment on edge devices remains challenging due to substantial computational and memory requirements. To this end, we present DnLUT, an ultra-efficient lookup table-based framework that achieves high-quality color image denoising with minimal resource consumption. Our key innovation lies in two complementary components: a Pairwise Channel Mixer (PCM) that effectively captures inter-channel correlations and spatial dependencies in parallel, and a novel L-shaped convolution design that maximizes receptive field coverage while minimizing storage overhead. By converting these components into optimized lookup tables post-training, DnLUT achieves remarkable efficiency - requiring only 500KB storage and 0.1% energy consumption compared to its CNN contestant DnCNN, while delivering 20x faster inference. Extensive experiments demonstrate that DnLUT outperforms all existing LUT-based methods by over 1dB in PSNR, establishing a new state-of-the-art in resource-efficient color image denoising.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_DnLUT_Ultra-Efficient_Color_Image_Denoising_via_Channel-Aware_Lookup_Tables_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Sidi and Huang, Binxiao and Zhang, Yulun and Yu, Dahai and Yang, Yujiu and Wong, Ngai},
    title     = {DnLUT: Ultra-Efficient Color Image Denoising via Channel-Aware Lookup Tables},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7582-7591}
}

raw_affiliation:
[{"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shanghai Jiaotong University", "city": "Shanghai", "country": "China"}, {"affiliation": "TCL Corporate Research", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["The University of Hong Kong", "Shanghai Jiaotong University", "TCL Corporate Research", "Tsinghua University"]


authors: Kyungmin Jo, Jooyeol Yun, Jaegul Choo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23595-23603
affiliations: ["Korea Advanced Institute of Science and Technology (KAIST)"]
cities: ["Daejeon"]
countries: ["Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jo_Devil_is_in_the_Detail_Towards_Injecting_Fine_Details_of_CVPR_2025_paper.html

title:
Devil is in the Detail: Towards Injecting Fine Details of Image Prompt in Image Generation via Conflict-free Guidance and Stratified Attention

abstract:
While large-scale text-to-image diffusion models enable the generation of high-quality, diverse images from text prompts, these prompts struggle to capture intricate details, such as textures, preventing the user intent from being reflected. This limitation has led to efforts to generate images conditioned on user-provided images, referred to as image prompts. Recent work modifies the self-attention mechanism to impose image conditions in generated images by replacing or concatenating the keys and values from the image prompt. This enables the self-attention layer to work like a cross-attention layer, generally used to incorporate text prompts.In this paper, we identify two common issues in existing methods of modifying self-attention that hinder diffusion models from reflecting the image prompt. By addressing these issues, we propose a novel method that generates images that properly reflect the details of image prompts. First, existing approaches often neglect the importance of image prompts in classifier-free guidance, which directs the model towards the intended conditions and away from those undesirable. Specifically, current methods use image prompts as both desired and undesired conditions, causing conflicting signals. To resolve this, we propose conflict-free guidance by using image prompts only as desired conditions, ensuring that the generated image faithfully reflects the image prompt.In addition, we observe that the two most common self-attention modifications involve a trade-off between the realism of the generated image and alignment with the image prompt, achieved by selectively using keys and values from both images. Specifically, selecting more keys and values from the image prompt improves alignment, while selecting more from the generated image enhances realism. To balance both, we propose an alternative self-attention modification method, Stratified Attention, which jointly uses keys and values from both images rather than selecting between them.Through extensive experiments across three distinct image generation tasks, we demonstrate that the proposed method outperforms existing image-prompting models in faithfully reflecting the image prompt.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jo_Devil_is_in_the_Detail_Towards_Injecting_Fine_Details_of_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jo_Devil_is_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jo_2025_CVPR,
    author    = {Jo, Kyungmin and Yun, Jooyeol and Choo, Jaegul},
    title     = {Devil is in the Detail: Towards Injecting Fine Details of Image Prompt in Image Generation via Conflict-free Guidance and Stratified Attention},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23595-23603}
}

raw_affiliation:
[{"affiliation": "Korea Advanced Institute of Science and Technology (KAIST)", "city": "Daejeon", "country": "Korea"}]


title: D^3-Human: Dynamic Disentangled Digital Human from Monocular Video
authors: Honghu Chen, Bo Peng, Yunfan Tao, Juyong Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10836-10846
affiliations: ["University of Science and Technology of China"]
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_D3-Human_Dynamic_Disentangled_Digital_Human_from_Monocular_Video_CVPR_2025_paper.html

abstract:
We introduce \text D ^3\text -Human , a method for reconstructing Dynamic Disentangled Digital Human geometry from monocular videos. Past monocular video human reconstruction primarily focuses on reconstructing undecoupled clothed human bodies or only reconstructing clothing, making it difficult to apply directly in applications such as animation production. The challenge in reconstructing decoupled clothing and body lies in the occlusion caused by clothing over the body. To this end, the details of the visible area and the plausibility of the invisible area must be ensured during the reconstruction process. Our proposed method combines explicit and implicit representations to model the decoupled clothed human body, leveraging the robustness of explicit representations and the flexibility of implicit representations. Specifically, we reconstruct the visible region as SDF and propose a novel human manifold signed distance field (hmSDF) to segment the visible clothing and visible body, and then merge the visible and invisible body. Extensive experimental results demonstrate that, compared with existing reconstruction schemes, \text D ^3\text -Human  can achieve high-quality decoupled reconstruction of the human body wearing different clothing, and can be directly applied to clothing transfer and animation production.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_D3-Human_Dynamic_Disentangled_Digital_Human_from_Monocular_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_D3-Human_Dynamic_Disentangled_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Honghu and Peng, Bo and Tao, Yunfan and Zhang, Juyong},
    title     = {D{\textasciicircum}3-Human: Dynamic Disentangled Digital Human from Monocular Video},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10836-10846}
}

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


title: BiM-VFI: Bidirectional Motion Field-Guided Frame Interpolation for Video with Non-uniform Motions
authors: Wonyong Seo, Jihyong Oh, Munchurl Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7244-7253
affiliations: ["KAIST*", "Chung-Ang University"]
cities: ["Daejeon", "Seoul"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Seo_BiM-VFI_Bidirectional_Motion_Field-Guided_Frame_Interpolation_for_Video_with_Non-uniform_CVPR_2025_paper.html

abstract:
Existing Video Frame interpolation (VFI) models tend to suffer from time-to-location ambiguity when trained with video of non-uniform motions, such as accelerating, decelerating, and changing directions, which often yield blurred interpolated frames.In this paper, we propose (i) a novel motion description map, Bidirectional Motion field (BiM), to effectively describe non-uniform motions;  (ii) a BiM-guided Flow Net (BiMFN)  with Content-Aware Upsampling Network (CAUN) for precise optical flow estimation; and (iii) Knowledge Distillation for VFI-centric Flow supervision (KDVCF) to supervise the motion estimation of VFI model with VFI-centric teacher flows.The proposed VFI is called a Bidirectional Motion field-guided VFI (BiM-VFI) model.Extensive experiments show that our BiM-VFI model significantly surpasses the recent state-of-the-art VFI methods by 26% and 45% improvements in LPIPS and STLPIPS respectively, yielding interpolated frames with much fewer blurs at arbitrary time instances.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Seo_BiM-VFI_Bidirectional_Motion_Field-Guided_Frame_Interpolation_for_Video_with_Non-uniform_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Seo_BiM-VFI_Bidirectional_Motion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Seo_2025_CVPR,
    author    = {Seo, Wonyong and Oh, Jihyong and Kim, Munchurl},
    title     = {BiM-VFI: Bidirectional Motion Field-Guided Frame Interpolation for Video with Non-uniform Motions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7244-7253}
}

raw_affiliation:
[{"affiliation": "KAIST*", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Chung-Ang University", "city": "Seoul", "country": "South Korea"}]


title: Curriculum Coarse-to-Fine Selection for High-IPC Dataset Distillation
authors: Yanda Chen, Gongwei Chen, Miao Zhang, Weili Guan, Liqiang Nie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20437-20446
affiliations: ["School of Computer Science and T echnology, Harbin Institute of T echnology, Shenzhen"]
cities: ["Shenzhen"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Curriculum_Coarse-to-Fine_Selection_for_High-IPC_Dataset_Distillation_CVPR_2025_paper.html

abstract:
Dataset distillation (DD) excels in synthesizing a small number of images per class (IPC) but struggles to maintain its effectiveness in high-IPC settings. Recent works on dataset distillation demonstrate that combining distilled and real data can mitigate the effectiveness decay. However, our analysis of the combination paradigm reveals that the current one-shot and independent selection mechanism induces an incompatibility issue between distilled and real images. To address this issue, we introduce a novel curriculum coarse-to-fine selection (CCFS) method for efficient high-IPC dataset distillation. CCFS employs a curriculum selection framework for real data selection, where we leverage a coarse-to-fine strategy to select appropriate real data based on the current synthetic dataset in each curriculum. Extensive experiments validate CCFS, surpassing the state-of-the-art by +6.6% on CIFAR-10, +5.8% on CIFAR-100, and +3.4% on Tiny-ImageNet under high-IPC settings. Notably, CCFS achieves 60.2% test accuracy on ResNet-18 with a 20% compression ratio of Tiny-ImageNet, closely matching full-dataset training with only 0.3% degradation. Code: https://github.com/CYDaaa30/CCFS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Curriculum_Coarse-to-Fine_Selection_for_High-IPC_Dataset_Distillation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Curriculum_Coarse-to-Fine_Selection_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Yanda and Chen, Gongwei and Zhang, Miao and Guan, Weili and Nie, Liqiang},
    title     = {Curriculum Coarse-to-Fine Selection for High-IPC Dataset Distillation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20437-20446}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and T echnology, Harbin Institute of T echnology, Shenzhen", "city": "Shenzhen", "country": "China"}]


authors: Yuguang Li, Ivaylo Boyadzhiev, Zixuan Liu, Linda Shapiro, Alex Colburn
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16785-16795
affiliations: ["University of Washington", "Zillow Group"]
cities: ["Seattle", "Seattle"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_BADGR_Bundle_Adjustment_Diffusion_Conditioned_by_Gradients_for_Wide-Baseline_Floor_CVPR_2025_paper.html

title:
BADGR: Bundle Adjustment Diffusion Conditioned by Gradients for Wide-Baseline Floor Plan Reconstruction

abstract:
Reconstructing precise camera poses and floor plan layouts from wide-baseline RGB panoramas is a difficult and unsolved problem. We introduce BADGR, a novel diffusion model that jointly performs reconstruction and bundle adjustment (BA) to refine poses and layouts from a coarse state, using 1D floor boundary predictions from dozens of sparsely captured images. Unlike guided diffusion models, BADGR is conditioned on dense per-column outputs from a single-step Levenberg Marquardt (LM) optimizer and is trained to predict camera and wall positions, while minimizing reprojection errors for view consistency. The objective of layout generation from denoising diffusion process complements BA optimization by providing additional learned layout-structural constraints on top of the co-visible features across images. These constraints help BADGR make plausible guesses about spatial relationships, which constrain the pose graph, such as wall adjacency and collinearity, while also learning to mitigate errors from dense boundary observations using global context. BADGR trains exclusively on 2D floor plans, simplifying data acquisition, enabling robust augmentation, and supporting a variety of input densities. Our experiments validate our method, which significantly outperforms the state-of-the-art pose and floor plan layout reconstruction with different input densities.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_BADGR_Bundle_Adjustment_Diffusion_Conditioned_by_Gradients_for_Wide-Baseline_Floor_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_BADGR_Bundle_Adjustment_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yuguang and Boyadzhiev, Ivaylo and Liu, Zixuan and Shapiro, Linda and Colburn, Alex},
    title     = {BADGR: Bundle Adjustment Diffusion Conditioned by Gradients for Wide-Baseline Floor Plan Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16785-16795}
}

raw_affiliation:
[{"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "Zillow Group", "city": "Seattle", "country": "USA"}]


authors: Jongseong Bae, Junwoo Ha, Ha Young Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11939-11948
cities: ["Seoul", "Seoul"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bae_Three_Cars_Approaching_within_100m_Enhancing_Distant_Geometry_by_Tri-Axis_CVPR_2025_paper.html

title:
Three Cars Approaching within 100m! Enhancing Distant Geometry by Tri-Axis Voxel Scanning for Camera-based Semantic Scene Completion

abstract:
Camera-based Semantic Scene Completion (SSC) is gaining attentions in the 3D perception field. However, properties such as perspective and occlusion lead to the underestimation of the geometry in distant regions, posing a critical issue for safety-focused autonomous driving systems. To tackle this, we propose ScanSSC, a novel camera-based SSC model composed of a Scan Module and Scan Loss, both designed to enhance distant scenes by leveraging context from near-viewpoint scenes. The Scan Module uses axis-wise masked attention, where each axis employing a near-to-far cascade masking that enables distant voxels to capture relationships with preceding voxels. In addition, the Scan Loss computes the cross-entropy along each axis between cumulative logits and corresponding class distributions in a near-to-far direction, thereby propagating rich context-aware signals to distant voxels. Leveraging the synergy between these components, ScanSSC achieves state-of-the-art performance, with IoUs of 44.54 and 48.29, and mIoUs of 17.40 and 20.14 on the SemanticKITTI and SSCBench-KITTI-360 benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bae_Three_Cars_Approaching_within_100m_Enhancing_Distant_Geometry_by_Tri-Axis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bae_Three_Cars_Approaching_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bae_2025_CVPR,
    author    = {Bae, Jongseong and Ha, Junwoo and Kim, Ha Young},
    title     = {Three Cars Approaching within 100m! Enhancing Distant Geometry by Tri-Axis Voxel Scanning for Camera-based Semantic Scene Completion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11939-11948}
}

raw_affiliation:
[{"affiliation": "Department of Arti\ufb01cial Intelligence, Y onsei University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Graduate School of Information, Y onsei University", "city": "Seoul", "country": "South Korea"}]

affiliations:
["Department of Arti\ufb01cial Intelligence, Y onsei University", "Graduate School of Information, Y onsei University"]


title: MetaShadow: Object-Centered Shadow Detection, Removal, and Synthesis
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28252-28262
affiliations: ["Adobe Research", "The Chinese University of Hong Kong"]
cities: ["San Jose", "Hong Kong"]
countries: ["USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_MetaShadow_Object-Centered_Shadow_Detection_Removal_and_Synthesis_CVPR_2025_paper.html

authors:
Tianyu Wang, Jianming Zhang, Haitian Zheng, Zhihong Ding, Scott Cohen, Zhe Lin, Wei Xiong, Chi-Wing Fu, Luis Figueroa, Soo Ye Kim

abstract:
Shadows are often underconsidered or even ignored in image editing applications, limiting the realism of the edited results. In this paper, we introduce MetaShadow, a three-in-one versatile framework that enables detection, removal, and controllable synthesis of shadows in natural images in an object-centered fashion. MetaShadow combines the strengths of two cooperative components: Shadow Analyzer, for object-centered shadow detection and removal, and Shadow Synthesizer, for reference-based controllable shadow synthesis. Notably, we optimize the learning of the intermediate features from Shadow Analyzer to guide Shadow Synthesizer to generate more realistic shadows that blend seamlessly with the scene. Extensive evaluations on multiple shadow benchmark datasets show significant improvements of MetaShadow over the existing state-of-the-art methods on object-centered shadow detection, removal, and synthesis. MetaShadow excels in supporting imageediting tasks such as object removal, relocation, and insertion, pushing the boundaries of object-centered image editing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_MetaShadow_Object-Centered_Shadow_Detection_Removal_and_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_MetaShadow_Object-Centered_Shadow_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Tianyu and Zhang, Jianming and Zheng, Haitian and Ding, Zhihong and Cohen, Scott and Lin, Zhe and Xiong, Wei and Fu, Chi-Wing and Figueroa, Luis and Kim, Soo Ye},
    title     = {MetaShadow: Object-Centered Shadow Detection, Removal, and Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28252-28262}
}

raw_affiliation:
[{"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]


title: TANGO: Training-free Embodied AI Agents for Open-world Tasks
authors: Filippo Ziliotto, Tommaso Campari, Luciano Serafini, Lamberto Ballan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24603-24613
affiliations: ["University of Padova", "Fondazione Bruno Kessler (FBK)"]
cities: ["Padova", "Trento"]
countries: ["Italy", "Italy"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ziliotto_TANGO_Training-free_Embodied_AI_Agents_for_Open-world_Tasks_CVPR_2025_paper.html

abstract:
Large Language Models (LLMs) have demonstrated excellent capabilities in composing various modules together to create programs that can perform complex reasoning tasks on images. In this paper, we propose TANGO, an approach that extends the program composition via LLMs already observed for images, aiming to integrate those capabilities into embodied agents capable of observing and acting in the world. Specifically, by employing a simple PointGoal Navigation model combined with a memory-based exploration policy as a foundational primitive for guiding an agent through the world, we show how a single model can address diverse tasks without additional training. We task an LLM with composing the provided primitives to solve a specific task, using only a few in-context examples in the prompt. We evaluate our approach on three key Embodied AI tasks: Open-Set ObjectGoal Navigation, Multi-Modal Lifelong Navigation, and Open Embodied Question Answering, achieving state-of-the-art results without any specific fine-tuning in challenging zero-shot scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ziliotto_TANGO_Training-free_Embodied_AI_Agents_for_Open-world_Tasks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ziliotto_TANGO_Training-free_Embodied_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ziliotto_2025_CVPR,
    author    = {Ziliotto, Filippo and Campari, Tommaso and Serafini, Luciano and Ballan, Lamberto},
    title     = {TANGO: Training-free Embodied AI Agents for Open-world Tasks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24603-24613}
}

raw_affiliation:
[{"affiliation": "University of Padova", "city": "Padova", "country": "Italy"}, {"affiliation": "Fondazione Bruno Kessler (FBK)", "city": "Trento", "country": "Italy"}]


title: SATA: Spatial Autocorrelation Token Analysis for Enhancing the Robustness of Vision Transformers
authors: Nick Nikzad, Yi Liao, Yongsheng Gao, Jun Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9730-9739
raw_affiliation: [{"affiliation": "Griffith University", "city": "Queensland", "country": "Australia"}]
affiliations: ["Griffith University"]
cities: ["Queensland"]
countries: ["Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nikzad_SATA_Spatial_Autocorrelation_Token_Analysis_for_Enhancing_the_Robustness_of_CVPR_2025_paper.html

abstract:
Over the past few years, vision transformers (ViTs) have consistently demonstrated remarkable performance across various visual recognition tasks. However, attempts to enhance their robustness have yielded limited success, mainly focusing on different training strategies, input patch augmentation, or network structural enhancements. These approaches often involve extensive training and fine-tuning, which are time-consuming and resource-intensive. To tackle these obstacles, we introduce a novel approach named Spatial Autocorrelation Token Analysis (SATA). By harnessing spatial relationships between token features, SATA enhances both the representational capacity and robustness of ViT models. This is achieved through the analysis and grouping of tokens according to their spatial autocorrelation scores prior to their input into the Feed-Forward Network (FFN) block of the self-attention mechanism. Importantly, SATA seamlessly integrates into existing pre-trained ViT baselines without requiring retraining or additional fine-tuning, while concurrently improving efficiency by reducing the computational load of the FFN units. Experimental results show that the baseline ViTs enhanced with SATA not only achieve a new state-of-the-art top-1 accuracy on ImageNet-1K image classification (94.9%) but also establish new state-of-the-art performance across multiple robustness benchmarks, including ImageNet-A (top-1=63.6%), ImageNet-R (top-1=79.2%), and ImageNet-C (mCE=13.6%), all without requiring additional training or fine-tuning of baseline models. Availability: https://github.com/nick-nikzad/SATA

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nikzad_SATA_Spatial_Autocorrelation_Token_Analysis_for_Enhancing_the_Robustness_of_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nikzad_SATA_Spatial_Autocorrelation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nikzad_2025_CVPR,
    author    = {Nikzad, Nick and Liao, Yi and Gao, Yongsheng and Zhou, Jun},
    title     = {SATA: Spatial Autocorrelation Token Analysis for Enhancing the Robustness of Vision Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9730-9739}
}


title: DViN: Dynamic Visual Routing Network for Weakly Supervised Referring Expression Comprehension
authors: Xiaofu Chen, Yaxin Luo, Gen Luo, Jiayi Ji, Henghui Ding, Yiyi Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14347-14357
affiliations: ["MBZUAI", "OpenGVLab, Shanghai AI Laboratory", "Xiamen University", "Fudan University"]
cities: ["Abu Dhabi", "Shanghai", "Xiamen", "Shanghai"]
countries: ["UAE", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_DViN_Dynamic_Visual_Routing_Network_for_Weakly_Supervised_Referring_Expression_CVPR_2025_paper.html

abstract:
In this paper, we focus on weakly supervised referring expression comprehension (REC), and identify that the lack of fine-grained visual capability greatly limits the upper performance bound of existing methods. To address this issue, we propose a novel framework for weakly supervised REC, namely Dynamic Visual routing Network (DViN), which overcomes the visual shortcomings from the perspective of feature combination and alignment. In particular, DViN is equipped with a novel sparse routing mechanism to efficiently combine features of multiple visual encoders in a dynamic manner, thus improving the visual descriptive power. Besides, we further propose an innovative weakly supervised objective, namely Routing-based Feature Alignment (RFA), which facilitates the visual understanding of routed features through the intra-modal and inter-modal alignment. To validate DViN, we conduct extensive experiments on four REC benchmark datasets. Experiments demonstrate that DViN achieves state-of-the-art results on four benchmarks while maintaining competitive inference efficiency. Besides, the strong generalization ability of DViN is also validated on weakly supervised referring expression segmentation. Source codes are anonymously released at: https://anonymous.4open.science/r/DViN-7736.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_DViN_Dynamic_Visual_Routing_Network_for_Weakly_Supervised_Referring_Expression_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Xiaofu and Luo, Yaxin and Luo, Gen and Ji, Jiayi and Ding, Henghui and Zhou, Yiyi},
    title     = {DViN: Dynamic Visual Routing Network for Weakly Supervised Referring Expression Comprehension},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14347-14357}
}

raw_affiliation:
[{"affiliation": "MBZUAI", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "OpenGVLab, Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}]


title: Nested Diffusion Models Using Hierarchical Latent Priors
authors: Xiao Zhang, Ruoxi Jiang, Rebecca Willett, Michael Maire
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2502-2512
affiliations: ["1", "2"]
cities: ["Unknown", "Unknown"]
countries: ["Unknown", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Nested_Diffusion_Models_Using_Hierarchical_Latent_Priors_CVPR_2025_paper.html

abstract:
We introduce nested diffusion models, an efficient and powerful hierarchical generative framework that substantially enhances the generation quality of diffusion models, particularly for images of complex scenes. Our approach employs a series of diffusion models to progressively generate latent variables at different semantic levels. Each model in this series is conditioned on the output of the preceding higher-level models, culminating in image generation. Hierarchical latent variables guide the generation process along predefined semantic pathways, allowing our approach to capture intricate structural details. To construct these latent variables, we leverage a pre-trained visual encoder, which learns strong semantic visual representations, and modulate its capacity via dimensionality reduction and noise injection. Across multiple datasets, our system demonstrates significant enhancements in image quality for both unconditional and class/text conditional generation. Moreover, our unconditional generation system substantially outperforms the baseline conditional system. These advancements incur minimal computational overhead as the more abstract levels of our hierarchy work with lower-dimensional representations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Nested_Diffusion_Models_Using_Hierarchical_Latent_Priors_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Nested_Diffusion_Models_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Xiao and Jiang, Ruoxi and Willett, Rebecca and Maire, Michael},
    title     = {Nested Diffusion Models Using Hierarchical Latent Priors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2502-2512}
}

raw_affiliation:
[{"affiliation": "1", "city": "Unknown", "country": "Unknown"}, {"affiliation": "2", "city": "Unknown", "country": "Unknown"}]


title: A Theory of Learning Unified Model via Knowledge Integration from Label Space Varying Domains
authors: Dexuan Zhang, Thomas Westfechtel, Tatsuya Harada
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10142-10152
affiliations: ["The University of T okyo", "RIKEN"]
cities: ["Tokyo", "Wako"]
countries: ["Japan", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_A_Theory_of_Learning_Unified_Model_via_Knowledge_Integration_from_CVPR_2025_paper.html

abstract:
Existing domain adaptation systems can hardly be applied to real-world problems with new classes presenting at deployment time, especially regarding source-free scenarios where multiple source domains do not share the label space despite being given a few labeled target data. To address this, we consider a challenging problem: multi-source semi-supervised open-set domain adaptation and propose a learning theory via joint error, effectively tackling strong domain shift. To generalize the algorithm into source-free cases, we introdcue a computationally efficient and architecture-flexible attention-based feature generation module. Extensive experiments on various data sets demonstrate the significant improvement of our proposed algorithm over baselines.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_A_Theory_of_Learning_Unified_Model_via_Knowledge_Integration_from_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_A_Theory_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Dexuan and Westfechtel, Thomas and Harada, Tatsuya},
    title     = {A Theory of Learning Unified Model via Knowledge Integration from Label Space Varying Domains},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10142-10152}
}

raw_affiliation:
[{"affiliation": "The University of T okyo", "city": "Tokyo", "country": "Japan"}, {"affiliation": "RIKEN", "city": "Wako", "country": "Japan"}]


authors: R.D. Lin, Pengcheng Weng, Yinqiao Wang, Han Ding, Jinsong Han, Fei Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1429-1438
cities: ["Xi'an", "Xi'an", "Hangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_HiLoTs_High-Low_Temporal_Sensitive_Representation_Learning_for_Semi-Supervised_LiDAR_Segmentation_CVPR_2025_paper.html

title:
HiLoTs: High-Low Temporal Sensitive Representation Learning for Semi-Supervised LiDAR Segmentation in Autonomous Driving

abstract:
LiDAR point cloud semantic segmentation plays a crucial role in autonomous driving. In recent years, semi-supervised methods have gained popularity due to their significant reduction in annotation labor and time costs. Current semi-supervised methods typically focus on point cloud spatial distribution or consider short-term temporal representations, e.g., only two adjacent frames, often overlooking the rich long-term temporal properties inherent in autonomous driving scenarios. In driving experience, we observe that nearby objects, such as roads and vehicles, remain stable while driving, whereas distant objects exhibit greater variability in category and shape. This natural phenomenon is also captured by LiDAR, which reflects lower temporal sensitivity for nearby objects and higher sensitivity for distant ones. To leverage these characteristics, we propose HiLoTs, which learns high-temporal sensitivity and low-temporal sensitivity representations from continuous LiDAR frames. These representations are further enhanced and fused using a cross-attention mechanism. Additionally, we employ a teacher-student framework to align the representations learned by the labeled and unlabeled branches, effectively utilizing the large amounts of unlabeled data. Experimental results on the SemanticKITTI and nuScenes datasets demonstrate that our proposed HiLoTs outperforms state-of-the-art semi-supervised methods, and achieves performance close to LiDAR+Camera multimodal approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_HiLoTs_High-Low_Temporal_Sensitive_Representation_Learning_for_Semi-Supervised_LiDAR_Segmentation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, R.D. and Weng, Pengcheng and Wang, Yinqiao and Ding, Han and Han, Jinsong and Wang, Fei},
    title     = {HiLoTs: High-Low Temporal Sensitive Representation Learning for Semi-Supervised LiDAR Segmentation in Autonomous Driving},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1429-1438}
}

raw_affiliation:
[{"affiliation": "School of Software Engineering, Xi\u2019an Jiaotong University, China", "city": "Xi'an", "country": "China"}, {"affiliation": "School of Computer Science and Technology, Xi\u2019an Jiaotong University, China", "city": "Xi'an", "country": "China"}, {"affiliation": "College of Computer Science and Technology, Zhejiang University, China", "city": "Hangzhou", "country": "China"}]

affiliations:
["School of Software Engineering, Xi\u2019an Jiaotong University, China", "School of Computer Science and Technology, Xi\u2019an Jiaotong University, China", "College of Computer Science and Technology, Zhejiang University, China"]


title: Spiking Transformer with Spatial-Temporal Attention
authors: Donghyun Lee, Yuhang Li, Youngeun Kim, Shiting Xiao, Priyadarshini Panda
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13948-13958
affiliations: ["Department of Electrical Engineering, Yale University"]
cities: ["New Haven"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Spiking_Transformer_with_Spatial-Temporal_Attention_CVPR_2025_paper.html

abstract:
Spike-based Transformer presents a compelling and energy-efficient alternative to traditional Artificial Neural Network (ANN)-based Transformers, achieving impressive results through sparse binary computations. However, existing spike-based transformers predominantly focus on spatial attention while neglecting crucial temporal dependencies inherent in spike-based processing, leading to suboptimal feature representation and limited performance. To address this limitation, we propose Spiking Transformer with Spatial-Temporal Attention (STAtten), a simple and straightforward architecture that efficiently integrates both spatial and temporal information in the self-attention mechanism. STAtten introduces a block-wise computation strategy that processes information in spatial-temporal chunks, enabling comprehensive feature capture while maintaining the same computational complexity as previous spatial-only approaches. Our method can be seamlessly integrated into existing spike-based transformers without architectural overhaul. Extensive experiments demonstrate that STAtten significantly improves the performance of existing spike-based transformers across both static and neuromorphic datasets, including CIFAR10/100, ImageNet, CIFAR10-DVS, and N-Caltech101.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Spiking_Transformer_with_Spatial-Temporal_Attention_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Spiking_Transformer_with_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Donghyun and Li, Yuhang and Kim, Youngeun and Xiao, Shiting and Panda, Priyadarshini},
    title     = {Spiking Transformer with Spatial-Temporal Attention},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13948-13958}
}

raw_affiliation:
[{"affiliation": "Department of Electrical Engineering, Yale University", "city": "New Haven", "country": "USA"}]


title: Perceptual Video Compression with Neural Wrapping
authors: Muhammad Umar Karim Khan, Aaron Chadha, Mohammad Ashraful Anam, Yiannis Andreopoulos
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17743-17754
raw_affiliation: [{"affiliation": "Sony Interactive Entertainment", "city": "San Mateo", "country": "USA"}]
affiliations: ["Sony Interactive Entertainment"]
cities: ["San Mateo"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Khan_Perceptual_Video_Compression_with_Neural_Wrapping_CVPR_2025_paper.html

abstract:
Standard video codecs are rate-distortion optimization machines, where distortion is typically quantified using PSNR versus the source. However, it is now widely accepted that increasing PSNR does not necessarily translate to better visual quality. In this paper, a better balance between perception and fidelity is targeted, in order to provide for significant rate savings over state-of-the-art standards-based video codecs. Specifically, pre- and postprocessing neural networks are proposed that enhance the coding efficiency of standard video codecs when benchmarked with an array of well-established perceptual quality scores. These "neural wrapper" elements are end-to-end trained with a neural codec module serving as a differentiable proxy for standard video codecs. The codec proxy is jointly optimized with the pre- and post components, via a novel two-phase pretraining strategy and end-to-end iterative refinement with stop-gradient. This allows the neural pre- and postprocessor to learn to embed, remove and recover information in a codec-aware manner, thus improving its rate-quality performance. A single neural-wrapper model is thereby established and used for the entire rate-quality curve without needing any downscaling or upscaling. The trained model is tested with the AV1 and VVC standard codecs via an array of well-established objective quality scores (SSIM, MS-SSIM, VMAF, AVQT), as well as mean opinion scores (MOS) derived from ITU-T P.910 subjective testing. Experimental results show that the proposed approach improves all quality scores, with -18.5% average Bjontegaard Delta-rate (BD-rate) saving over all objective scores and MOS improvement over both standard codecs. This illustrates the significant potential of neural wrapper components  over standards-based video coding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Khan_Perceptual_Video_Compression_with_Neural_Wrapping_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Khan_Perceptual_Video_Compression_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Khan_2025_CVPR,
    author    = {Khan, Muhammad Umar Karim and Chadha, Aaron and Anam, Mohammad Ashraful and Andreopoulos, Yiannis},
    title     = {Perceptual Video Compression with Neural Wrapping},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17743-17754}
}


title: ViKIENet: Towards Efficient 3D Object Detection with Virtual Key Instance Enhanced Network
authors: Zhuochen Yu, Bijie Qiu, Andy W. H. Khong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11844-11853
cities: ["Singapore", "Singapore", "Singapore"]
countries: ["Singapore", "Singapore", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_ViKIENet_Towards_Efficient_3D_Object_Detection_with_Virtual_Key_Instance_CVPR_2025_paper.html

abstract:
The sparsity of point clouds and inadequacy of semantic information pose challenges to current LiDAR-only 3D object detection methods. Recent methods alleviate these challenges by converting RGB images into virtual points via depth completion to be fused with LiDAR points. Although these methods have shown outstanding results, they often introduce significant computation overhead due to the high density of virtual points and noise due to inaccurate depth completion. Besides, they do not thoroughly leverage semantic information from images. In this work, we propose the virtual key instance enhanced network (ViKIENet), a highly efficient and effective multi-modal feature fusion framework that fuses the features of virtual key instances (VKIs) and LiDAR points through multiple stages. Our contributions include three main components: semantic key instance selection (SKIS), virtual-instance-focused fusion (VIFF), and virtual-instance-to-real attention (VIRA). We also propose the extended version ViKIENet-R with VIFF-R which includes rotationally equivariant features. Experiment results show that ViKIENet and ViKIENet-R achieve significant improvements in detection performance on the KITTI, JRDB, and nuScenes datasets compared to existing works. On the KITTI dataset, ViKIENet and ViKIENet-R operate at 22.7 and 15.0 FPS, respectively. As of CVPR submission (Nov. 15th, 2024), ViKIENet ranks first on the car detection and orientation estimation leaderboard, while ViKIENet-R ranks second (compared with officially published papers) on the 3D car detection leaderboard.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_ViKIENet_Towards_Efficient_3D_Object_Detection_with_Virtual_Key_Instance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_ViKIENet_Towards_Efficient_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Zhuochen and Qiu, Bijie and Khong, Andy W. H.},
    title     = {ViKIENet: Towards Efficient 3D Object Detection with Virtual Key Instance Enhanced Network},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11844-11853}
}

raw_affiliation:
[{"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "School of Electrical and Electronic Engineering", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Lee Kong Chian School of Medicine", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Nanyang Technological University", "School of Electrical and Electronic Engineering", "Lee Kong Chian School of Medicine"]


title: DKDM: Data-Free Knowledge Distillation for Diffusion Models with Any Architecture
authors: Qianlong Xiang, Miao Zhang, Yuzhang Shang, Jianlong Wu, Yan Yan, Liqiang Nie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2955-2965
cities: ["Shenzhen", "Chicago", "Chicago"]
countries: ["China", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiang_DKDM_Data-Free_Knowledge_Distillation_for_Diffusion_Models_with_Any_Architecture_CVPR_2025_paper.html

abstract:
Diffusion models (DMs) have demonstrated exceptional generative capabilities across various domains, including image, video, and so on. A key factor contributing to their effectiveness is the high quantity and quality of data used during training. However, mainstream DMs now consume increasingly large amounts of data. For example, training a Stable Diffusion model requires billions of image-text pairs. This enormous data requirement poses significant challenges for training large DMs due to high data acquisition costs and storage expenses. To alleviate this data burden, we propose a novel scenario: using existing DMs as data sources to train new DMs with any architecture. We refer to this scenario as Data-Free Knowledge Distillation for Diffusion Models (DKDM), where the generative ability of DMs is transferred to new ones in a data-free manner. To tackle this challenge, we make two main contributions. First, we introduce a DKDM objective that enables the training of new DMs via distillation, without requiring access to the data. Second, we develop a dynamic iterative distillation method that efficiently extracts time-domain knowledge from existing DMs, enabling direct retrieval of training data without the need for a prolonged generative process. To the best of our knowledge, we are the first to explore this scenario. Experimental results demonstrate that our data-free approach not only achieves competitive generative performance but also, in some instances, outperforms models trained with the entire dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiang_DKDM_Data-Free_Knowledge_Distillation_for_Diffusion_Models_with_Any_Architecture_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiang_DKDM_Data-Free_Knowledge_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xiang_2025_CVPR,
    author    = {Xiang, Qianlong and Zhang, Miao and Shang, Yuzhang and Wu, Jianlong and Yan, Yan and Nie, Liqiang},
    title     = {DKDM: Data-Free Knowledge Distillation for Diffusion Models with Any Architecture},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2955-2965}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of Technology (Shenzhen)", "city": "Shenzhen", "country": "China"}, {"affiliation": "Illinois Institute of Technology", "city": "Chicago", "country": "USA"}, {"affiliation": "University of Illinois Chicago", "city": "Chicago", "country": "USA"}]

affiliations:
["Harbin Institute of Technology (Shenzhen)", "Illinois Institute of Technology", "University of Illinois Chicago"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9361-9371
cities: ["Beijing", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jia_SymDPO_Boosting_In-Context_Learning_of_Large_Multimodal_Models_with_Symbol_CVPR_2025_paper.html

title:
SymDPO: Boosting In-Context Learning of Large Multimodal Models with Symbol Demonstration Direct Preference Optimization

authors:
Hongrui Jia, Chaoya Jiang, Haiyang Xu, Wei Ye, Mengfan Dong, Ming Yan, Ji Zhang, Fei Huang, Shikun Zhang

abstract:
As language models continue to scale, Large Language Models (LLMs) have exhibited emerging capabilities in In-Context Learning (ICL), enabling them to solve language tasks by prefixing a few in-context demonstrations (ICDs) as context. Inspired by these advancements, researchers have extended these techniques to develop Large Multimodal Models (LMMs) with ICL capabilities. However, existing LMMs face a critical issue: they often fail to effectively leverage the visual context in multimodal demonstrations and instead simply follow textual patterns. This indicates that LMMs do not achieve effective alignment between multimodal demonstrations and model outputs. To address this problem, we propose Symbol Demonstration Direct Preference Optimization (SymDPO). Specifically, SymDPO aims to break the traditional paradigm of constructing multimodal demonstrations by using random symbols to replace text answers within instances. This forces the model to carefully understand the demonstration images and establish a relationship between the images and the symbols to answer questions correctly. We validate the effectiveness of this method on multiple benchmarks, demonstrating that with SymDPO, LMMs can more effectively understand the multimodal context within examples and utilize this knowledge to answer questions better. Code is available at https://github.com/APiaoG/SymDPO.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jia_SymDPO_Boosting_In-Context_Learning_of_Large_Multimodal_Models_with_Symbol_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jia_SymDPO_Boosting_In-Context_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Jia_2025_CVPR,
    author    = {Jia, Hongrui and Jiang, Chaoya and Xu, Haiyang and Ye, Wei and Dong, Mengfan and Yan, Ming and Zhang, Ji and Huang, Fei and Zhang, Shikun},
    title     = {SymDPO: Boosting In-Context Learning of Large Multimodal Models with Symbol Demonstration Direct Preference Optimization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9361-9371}
}

raw_affiliation:
[{"affiliation": "National Engineering Research Center for Software Engineering, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Alibaba Group", "city": "Hangzhou", "country": "China"}]

affiliations:
["National Engineering Research Center for Software Engineering, Peking University", "Alibaba Group"]


authors: Zhaoyi Liu, Huan Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25060-25070
affiliations: ["University of Illinois Urbana-Champaign (UIUC)"]
cities: ["Urbana"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Stealthy_Backdoor_Attack_in_Self-Supervised_Learning_Vision_Encoders_for_Large_CVPR_2025_paper.html

title:
Stealthy Backdoor Attack in Self-Supervised Learning Vision Encoders for Large Vision Language Models

abstract:
Self-supervised learning (SSL) vision encoders learn high-quality image representations and thus have become a vital part of developing vision modality of large vision language models (LVLMs). Due to the high cost of training such encoders, pre-trained encoders are widely shared and deployed into many LVLMs, which are security-critical or bear societal significance. Under this practical scenario, we reveal a new backdoor threat that significant visual hallucinations can be induced into these LVLMs by merely compromising vision encoders. Because of the sharing and reuse of these encoders, many downstream LVLMs may inherit backdoor behaviors from encoders, leading to widespread backdoors. In this work, we propose BadVision, the first method to exploit this vulnerability in SSL vision encoders for LVLMs with novel trigger optimization and backdoor learning techniques. We evaluate BadVision on two types of SSL encoders and LVLMs across eight benchmarks. We show that BadVision effectively drives the LVLMs to attacker-chosen hallucination with over 99% attack success rate, causing a 77.6% relative visual understanding error while maintaining the stealthiness. SoTA backdoor detection methods cannot detect our attack effectively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Stealthy_Backdoor_Attack_in_Self-Supervised_Learning_Vision_Encoders_for_Large_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Stealthy_Backdoor_Attack_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Zhaoyi and Zhang, Huan},
    title     = {Stealthy Backdoor Attack in Self-Supervised Learning Vision Encoders for Large Vision Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25060-25070}
}

raw_affiliation:
[{"affiliation": "University of Illinois Urbana-Champaign (UIUC)", "city": "Urbana", "country": "USA"}]


title: Data-free Universal Adversarial Perturbation with Pseudo-semantic Prior
authors: Chanhui Lee, Yeonghwan Song, Jeany Son
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13907-13916
raw_affiliation: [{"affiliation": "AI Graduate School, GIST", "city": "Gwangju", "country": "South Korea"}]
affiliations: ["AI Graduate School, GIST"]
cities: ["Gwangju"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Data-free_Universal_Adversarial_Perturbation_with_Pseudo-semantic_Prior_CVPR_2025_paper.html

abstract:
Data-free Universal Adversarial Perturbation (UAP) is an image-agnostic adversarial attack that deceives deep neural networks using a single perturbation generated solely from random noise without relying on data priors. However, traditional data-free UAP methods often suffer from limited transferability due to the absence of semantic content in random noise. To address this issue, we propose a novel data-free universal attack method that recursively extracts pseudo-semantic priors directly from the UAPs during training to enrich the semantic content within the data-free UAP framework. Our approach effectively leverages latent semantic information within UAPs via region sampling, enabling successful input transformations--typically ineffective in traditional data-free UAP methods due to the lack of semantic cues--and significantly enhancing black-box transferability. Furthermore, we introduce a sample reweighting technique to mitigate potential imbalances from random sampling and transformations, emphasizing hard examples less affected by the UAPs. Comprehensive experiments on ImageNet show that our method achieves state-of-the-art performance in average fooling rate by a substantial margin, notably improves attack transferability across various CNN architectures compared to existing data-free UAP methods, and even surpasses data-dependent UAP methods. Code is available at: https://github.com/ChnanChan/PSP-UAP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Data-free_Universal_Adversarial_Perturbation_with_Pseudo-semantic_Prior_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Data-free_Universal_Adversarial_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Chanhui and Song, Yeonghwan and Son, Jeany},
    title     = {Data-free Universal Adversarial Perturbation with Pseudo-semantic Prior},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13907-13916}
}


title: Debiasing Multimodal Large Language Models via Noise-Aware Preference Optimization
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9423-9433
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Debiasing_Multimodal_Large_Language_Models_via_Noise-Aware_Preference_Optimization_CVPR_2025_paper.html

authors:
Zefeng Zhang, Hengzhu Tang, Jiawei Sheng, Zhenyu Zhang, Yiming Ren, Zhenyang Li, Dawei Yin, Duohe Ma, Tingwen Liu

abstract:
Multimodal Large Language Models (MLLMs) excel in various tasks, yet often struggle with modality bias, tending to rely heavily on a single modality or prior knowledge when generating responses. In this paper, we propose a debiased preference optimization dataset, RLAIF-V-Bias, and introduce a Noise-Aware Preference Optimization (NAPO) algorithm. Specifically, we first construct the dataset by introducing perturbations to reduce the informational content of certain modalities, prompting the model to overly rely on a specific modality when generating responses. To address the inevitable noise in automatically constructed data, we combine the noise-robust Mean Absolute Error (MAE) with the Binary Cross-Entropy (BCE) in Direct Preference Optimization (DPO) using a negative Box-Cox transformation and dynamically adjust the algorithm's noise robustness based on the evaluated noise levels in the data.Extensive experiments validate our approach, demonstrating not only its effectiveness in mitigating modality bias but also its significant role in minimizing hallucinations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Debiasing_Multimodal_Large_Language_Models_via_Noise-Aware_Preference_Optimization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Debiasing_Multimodal_Large_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zefeng and Tang, Hengzhu and Sheng, Jiawei and Zhang, Zhenyu and Ren, Yiming and Li, Zhenyang and Yin, Dawei and Ma, Duohe and Liu, Tingwen},
    title     = {Debiasing Multimodal Large Language Models via Noise-Aware Preference Optimization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9423-9433}
}

raw_affiliation:
[{"affiliation": "Institute of Information Engineering, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Cyber Security, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Baidu Inc.", "city": "Beijing", "country": "China"}]

affiliations:
["Institute of Information Engineering, Chinese Academy of Sciences", "School of Cyber Security, University of Chinese Academy of Sciences", "Baidu Inc."]


title: SAM2-LOVE: Segment Anything Model 2 in Language-aided Audio-Visual Scenes
authors: Yuji Wang, Haoran Xu, Yong Liu, Jiaze Li, Yansong Tang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28932-28941
affiliations: ["Tsinghua Shenzhen International Graduate School, Tsinghua University", "ZJU"]
cities: ["Shenzhen", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_SAM2-LOVE_Segment_Anything_Model_2_in_Language-aided_Audio-Visual_Scenes_CVPR_2025_paper.html

abstract:
Reference Audio-Visual Segmentation (Ref-AVS) aims to provide a pixel-wise scene understanding in Language-aided Audio-Visual Scenes (LAVS). This task requires the model to continuously segment objects referred to by text and audio from a video. Previous dual-modality methods always fail due to the lack of a third modality and the existing triple-modality method struggles with spatio-temporal consistency, leading to the target shift of different frames. In this work, we introduce a novel framework, termed SAM2-LOVE, which integrates textual, audio, and visual representations into a learnable token to prompt and align SAM2 for achieving Ref-AVS in the LAVS. Technically, our approach includes a multimodal fusion module aimed at improving multimodal understanding of SAM2, as well as token propagation and accumulation strategies designed to enhance spatio-temporal consistency without forgetting historical information. We conducted extensive experiments to demonstrate that SAM2-LOVE outperforms the SOTA by 8.5% in J&F on the Ref-AVS benchmark and showcase the simplicity and effectiveness of the components. Our code will be available here.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_SAM2-LOVE_Segment_Anything_Model_2_in_Language-aided_Audio-Visual_Scenes_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_SAM2-LOVE_Segment_Anything_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yuji and Xu, Haoran and Liu, Yong and Li, Jiaze and Tang, Yansong},
    title     = {SAM2-LOVE: Segment Anything Model 2 in Language-aided Audio-Visual Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28932-28941}
}

raw_affiliation:
[{"affiliation": "Tsinghua Shenzhen International Graduate School, Tsinghua University", "city": "Shenzhen", "country": "China"}, {"affiliation": "ZJU", "city": "Hangzhou", "country": "China"}]


authors: Ziqin Huang, Gu Wang, Chenyangguang Zhang, Ruida Zhang, Xiu Li, Xiangyang Ji
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22055-22066
affiliations: ["Tsinghua University", "Pengcheng Laboratory"]
cities: ["Beijing", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_GIVEPose_Gradual_Intra-class_Variation_Elimination_for_RGB-based_Category-Level_Object_Pose_CVPR_2025_paper.html

title:
GIVEPose: Gradual Intra-class Variation Elimination for RGB-based Category-Level Object Pose Estimation

abstract:
Recent advances in RGBD-based category-level object pose estimation have been limited by their reliance on precise depth information, restricting their broader applicability. In response, RGB-based methods have been developed. Among these methods, geometry-guided pose regression that originated from instance-level tasks has demonstrated strong performance. However, we argue that the NOCS map is an inadequate intermediate representation for geometry-guided pose regression method, as its many-to-one correspondence with category-level pose introduces redundant instance-specific information, resulting in suboptimal results. This paper identifies the intra-class variation problem inherent in pose regression based solely on the NOCS map and proposes the Intra-class Variation-Free Consensus (IVFC) map, a novel coordinate representation generated from the category-level consensus model. By leveraging the complementary strengths of the NOCS map and the IVFC map, we introduce GIVEPose, a framework that implements Gradual Intra-class Variation Elimination for category-level object pose estimation. Extensive evaluations on both synthetic and real-world datasets demonstrate that GIVEPose significantly outperforms existing state-of-the-art RGB-based approaches, achieving substantial improvements in category-level object pose estimation. Our code is available at https://github.com/ziqin-h/GIVEPose.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_GIVEPose_Gradual_Intra-class_Variation_Elimination_for_RGB-based_Category-Level_Object_Pose_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_GIVEPose_Gradual_Intra-class_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Ziqin and Wang, Gu and Zhang, Chenyangguang and Zhang, Ruida and Li, Xiu and Ji, Xiangyang},
    title     = {GIVEPose: Gradual Intra-class Variation Elimination for RGB-based Category-Level Object Pose Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22055-22066}
}

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


title: FRAME: Floor-aligned Representation for Avatar Motion from Egocentric Video
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17497-17507
cities: ["Saarbr\u00fccken", "Saarbr\u00fccken", "Switzerland"]
countries: ["Germany", "Germany", "Switzerland"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Camiletto_FRAME_Floor-aligned_Representation_for_Avatar_Motion_from_Egocentric_Video_CVPR_2025_paper.html

authors:
Andrea Boscolo Camiletto, Jian Wang, Eduardo Alvarado, Rishabh Dabral, Thabo Beeler, Marc Habermann, Christian Theobalt

abstract:
Egocentric motion capture with a head-mounted body-facing stereo camera is crucial for VR and AR applications but presents significant challenges such as heavy occlusions and limited annotated real-world data. Existing methods rely on synthetic pretraining and struggle to generate smooth and accurate predictions in real-world settings, particularly for lower limbs. Our work addresses these limitations by introducing a lightweight VR-based data collection setup with on-board, real-time 6D pose tracking. Using this setup, we collected the most extensive real-world dataset for ego-facing ego-mounted cameras to date in size and motion variability. Effectively integrating this multimodal input -- device pose and camera feeds -- is challenging due to the differing characteristics of each data source. To address this, we propose FRAME, a simple yet effective architecture that combines device pose and camera feeds for state-of-the-art body pose prediction through geometrically sound multimodal integration and can run at 300 FPS on modern hardware. Lastly, we showcase a novel training strategy to enhance the model's generalization capabilities. Our approach exploits the problem's geometric properties, yielding high-quality motion capture free from common artifacts in prior works. Qualitative and quantitative evaluations, along with extensive comparisons, demonstrate the effectiveness of our method. Data, code, and CAD designs will be available at https://vcai.mpi-inf.mpg.de/projects/FRAME/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Camiletto_FRAME_Floor-aligned_Representation_for_Avatar_Motion_from_Egocentric_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Camiletto_FRAME_Floor-aligned_Representation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Camiletto_2025_CVPR,
    author    = {Camiletto, Andrea Boscolo and Wang, Jian and Alvarado, Eduardo and Dabral, Rishabh and Beeler, Thabo and Habermann, Marc and Theobalt, Christian},
    title     = {FRAME: Floor-aligned Representation for Avatar Motion from Egocentric Video},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17497-17507}
}

raw_affiliation:
[{"affiliation": "Max Planck Institute for Informatics", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "Saarbr\u00a8ucken Research Center for Visual Computing, Interaction and AI", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "Google", "city": "Switzerland", "country": "Switzerland"}]

affiliations:
["Max Planck Institute for Informatics", "Saarbr\u00a8ucken Research Center for Visual Computing, Interaction and AI", "Google"]


title: Sketch Down the FLOPs: Towards Efficient Networks for Human Sketch
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28383-28393
cities: ["Guildford", "Orlando"]
countries: ["United Kingdom", "United States"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sain_Sketch_Down_the_FLOPs_Towards_Efficient_Networks_for_Human_Sketch_CVPR_2025_paper.html

authors:
Aneeshan Sain, Subhajit Maity, Pinaki Nath Chowdhury, Shubhadeep Koley, Ayan Kumar Bhunia, Yi-Zhe Song

abstract:
As sketch research has collectively matured over time, its adaptation for at-mass commercialisation emerges on the immediate horizon. Despite an already mature research endeavour for photos, there is no research on the efficient inference specifically designed for sketch data. In this paper, we first demonstrate existing state-of-the-art efficient light-weight models designed for photos do not work on sketches. We then propose two sketch-specific components which work in a plug-n-play manner on any photo efficient network to adapt them to work on sketch data. We specifically chose fine-grained sketch-based image retrieval (FG-SBIR) as a demonstrator as the most recognised sketch problem with immediate commercial value. Technically speaking, we first propose a cross-modal knowledge distillation network to transfer existing photo efficient networks to be compatible with sketch, which brings down number of FLOPs and model parameters by 97.96% percent and 84.89% respectively. We then exploit the abstract trait of sketch to introduce a RL-based canvas selector that dynamically adjusts to the abstraction level which further cuts down number of FLOPs by two thirds. The end result is an overall reduction of 99.37% of FLOPs (from 40.18G to 0.254G) when compared with a full network, while retaining the accuracy (33.03% vs 32.77%) -- finally making an efficient network for the sparse sketch data that exhibit even fewer FLOPs than the best photo counterpart.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sain_Sketch_Down_the_FLOPs_Towards_Efficient_Networks_for_Human_Sketch_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sain_Sketch_Down_the_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Sain_2025_CVPR,
    author    = {Sain, Aneeshan and Maity, Subhajit and Chowdhury, Pinaki Nath and Koley, Shubhadeep and Bhunia, Ayan Kumar and Song, Yi-Zhe},
    title     = {Sketch Down the FLOPs: Towards Efficient Networks for Human Sketch},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28383-28393}
}

raw_affiliation:
[{"affiliation": "SketchX, CVSSP, University of Surrey, United Kingdom", "city": "Guildford", "country": "United Kingdom"}, {"affiliation": "Department of Computer Science, University of Central Florida, United States", "city": "Orlando", "country": "United States"}]

affiliations:
["SketchX, CVSSP, University of Surrey, United Kingdom", "Department of Computer Science, University of Central Florida, United States"]


title: Generalized Zero-Shot Classification via Semantics-Free Inter-Class Feature Generation
authors: Libiao Chen, Dong Nie, Junjun Pan, Jing Yan, Zhenyu Tang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20286-20295
cities: ["Beijing", "Unknown", "Beijing", "Zhengzhou"]
countries: ["China", "Unknown", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Generalized_Zero-Shot_Classification_via_Semantics-Free_Inter-Class_Feature_Generation_CVPR_2025_paper.html

abstract:
Generalized Zero-Shot Learning (GZSL) addresses the challenge of classifying unseen classes in the presence of seen classes by leveraging semantic attributes to bridge the gap for unseen classes. However, in image based disease classification, such as glioma sub-typing, distinguishing between classes using image semantic attributes can be challenging. To address this challenge, we introduce a novel GZSL method that eliminates the dependency on semantic information. Specifically, we propose that the primary of most classification in clinic is risk stratification, and classes are inherently ordered rather than purely categorical. Based on this insight, we present an inter-class feature augmentation (IFA) module, where distributions of different classes are ordered by their risk levels in a learned feature space using pre-defined joint conditional Gaussian distribution model. This ordering enables the generation of unseen class features through feature mixing of adjacent seen classes, effectively transforming the zero-shot learning problem into a supervised learning task. Our method eliminates the need for explicit semantic information, avoiding the cross-modal alignment between visual and semantic features. Moreover, the IFA module for GZSL requires no structural modifications to the existing classification models. In the experiment, both in-house and public datasets are used to evaluate our method across different tasks, including glioma subtyping, Alzheimer's disease (AD) classification and diabetic retinopathy classification. Experimental results demonstrate that our method outperforms the state-of-the-art GZSL methods with statistical significance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Generalized_Zero-Shot_Classification_via_Semantics-Free_Inter-Class_Feature_Generation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Libiao and Nie, Dong and Pan, Junjun and Yan, Jing and Tang, Zhenyu},
    title     = {Generalized Zero-Shot Classification via Semantics-Free Inter-Class Feature Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20286-20295}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Beihang University, Beijing, China", "city": "Beijing", "country": "China"}, {"affiliation": "TowerCloud Labs", "city": "Unknown", "country": "Unknown"}, {"affiliation": "State Key Laboratory of Virtual Reality Technology and System, Beihang University, Beijing, China", "city": "Beijing", "country": "China"}, {"affiliation": "The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China", "city": "Zhengzhou", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Beihang University, Beijing, China", "TowerCloud Labs", "State Key Laboratory of Virtual Reality Technology and System, Beihang University, Beijing, China", "The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China"]


title: Feat2GS: Probing Visual Foundation Models with Gaussian Splatting
authors: Yue Chen, Xingyu Chen, Anpei Chen, Gerard Pons-Moll, Yuliang Xiu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6348-6361
cities: ["Hangzhou", "Hangzhou", "T\n\"ubingen", "Saarbr\n\"ucken", "T\n\"ubingen"]
countries: ["China", "China", "Germany", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Feat2GS_Probing_Visual_Foundation_Models_with_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
Given that visual foundation models (VFMs) are trained on extensive datasets but often limited to 2D images, a natural question arises: how well do they understand the 3D world? With the differences in architecture and training protocols (i.e., objectives, proxy tasks), a unified framework to fairly and comprehensively probe their 3D awareness is urgently needed. Existing works on 3D probing suggest single-view 2.5D estimation (e.g., depth and normal) or two-view sparse 2D correspondence (e.g., matching and tracking). Unfortunately, these tasks ignore texture awareness, and require 3D data as ground-truth, which limits the scale and diversity of their evaluation set. To address these issues, we introduce Feat2GS, which readout 3D Gaussians attributes from VFM features extracted from unposed images. This allows us to probe 3D awareness for geometry and texture via novel view synthesis, without requiring 3D data. Additionally, the disentanglement of 3DGS parameters - geometry (x, a, S) and texture (c) - enables separate analysis of texture and geometry awareness. Under Feat2GS, we conduct extensive experiments to probe the 3D awareness of several VFMs, and investigate the ingredients that lead to a 3D aware VFM. Building on these findings, we develop several variants that achieve state-of-the-art across diverse datasets. This makes Feat2GS useful for probing VFMs, and as a simple-yet-effective baseline for novel-view synthesis. Code and data will be made available at fanegg.github.io/Feat2GS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Feat2GS_Probing_Visual_Foundation_Models_with_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Feat2GS_Probing_Visual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Yue and Chen, Xingyu and Chen, Anpei and Pons-Moll, Gerard and Xiu, Yuliang},
    title     = {Feat2GS: Probing Visual Foundation Models with Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6348-6361}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of T\n\"ubingen, T\n\"ubingen AI Center", "city": "T\n\"ubingen", "country": "Germany"}, {"affiliation": "Max Planck Institute for Informatics, Saarland Informatics Campus", "city": "Saarbr\n\"ucken", "country": "Germany"}, {"affiliation": "Max Planck Institute for Intelligent Systems", "city": "T\n\"ubingen", "country": "Germany"}]

affiliations:
["Zhejiang University", "Westlake University", "University of T\n\"ubingen, T\n\"ubingen AI Center", "Max Planck Institute for Informatics, Saarland Informatics Campus", "Max Planck Institute for Intelligent Systems"]


title: Multi-Modal Aerial-Ground Cross-View Place Recognition with Neural ODEs
authors: Sijie Wang, Rui She, Qiyu Kang, Siqi Li, Disheng Li, Tianyu Geng, Shangshu Yu, Wee Peng Tay
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11717-11728
cities: ["Singapore", "Beijing", "Hefei"]
countries: ["Singapore", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Multi-Modal_Aerial-Ground_Cross-View_Place_Recognition_with_Neural_ODEs_CVPR_2025_paper.html

abstract:
Place recognition (PR) aims at retrieving the query place from a database and plays a crucial role in various applications, including navigation, autonomous driving, and augmented reality. While previous multi-modal PR works have mainly focused on the same-view scenario in which ground-view descriptors are matched with a database of ground-view descriptors during inference, the multi-modal cross-view scenario, in which ground-view descriptors are matched with aerial-view descriptors in a database, remains under-explored. We propose AGPlace, a model that effectively integrates information from multi-modal ground sensors (cameras and LiDARs) to achieve accurate aerial-ground PR. AGPlace achieves effective aerial-ground cross-view PR by leveraging a manifold-based neural ordinary differential equation (ODE) framework with a multi-domain alignment loss. It outperforms existing state-of-the-art cross-view PR models on large-scale datasets. As most existing PR models are designed for ground-ground PR, we adapt these baselines into our cross-view pipeline. Experiments demonstrate that this direct adaptation performs worse than our overall model architecture AGPlace. AGPlace represents a significant advancement in multi-modal aerial-ground PR, with promising implications for real-world applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Multi-Modal_Aerial-Ground_Cross-View_Place_Recognition_with_Neural_ODEs_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Multi-Modal_Aerial-Ground_Cross-View_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Sijie and She, Rui and Kang, Qiyu and Li, Siqi and Li, Disheng and Geng, Tianyu and Yu, Shangshu and Tay, Wee Peng},
    title     = {Multi-Modal Aerial-Ground Cross-View Place Recognition with Neural ODEs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11717-11728}
}

raw_affiliation:
[{"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}]

affiliations:
["Nanyang Technological University", "Beihang University", "University of Science and Technology of China"]


authors: Saad Wazir, Daeyoung Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30861-30871
raw_affiliation: [{"affiliation": "School of Computing, KAIST", "city": "Daejeon", "country": "Republic of Korea"}]
affiliations: ["School of Computing, KAIST"]
cities: ["Daejeon"]
countries: ["Republic of Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wazir_Rethinking_Decoder_Design_Improving_Biomarker_Segmentation_Using_Depth-to-Space_Restoration_and_CVPR_2025_paper.html

title:
Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention

abstract:
Segmenting biomarkers in medical images is crucial for various biotech applications. Despite advances, Transformer and CNN based methods often struggle with variations in staining and morphology, limiting feature extraction. In medical image segmentation, where datasets often have limited sample availability, recent state-of-the-art (SOTA) methods achieve higher accuracy by leveraging pre-trained encoders, whereas end-to-end methods tend to underperform. This is due to challenges in effectively transferring rich multiscale features from encoders to decoders, as well as limitations in decoder efficiency. To address these issues, we propose an architecture that captures multi-scale local and global contextual information and a novel decoder design, which effectively integrates features from the encoder, emphasizes important channels and regions, and reconstructs spatial dimensions to enhance segmentation accuracy. Our method, compatible with various encoders, outperforms SOTA methods, as demonstrated by experiments on four datasets and ablation studies. Specifically, our method achieves absolute performance gains of 2.76% on MoNuSeg, 3.12% on DSB, 2.87% on Electron Microscopy, and 4.03% on TNBC datasets compared to existing SOTA methods. Code: https://github.com/saadwazir/MCADS-Decoder

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wazir_Rethinking_Decoder_Design_Improving_Biomarker_Segmentation_Using_Depth-to-Space_Restoration_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wazir_Rethinking_Decoder_Design_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wazir_2025_CVPR,
    author    = {Wazir, Saad and Kim, Daeyoung},
    title     = {Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30861-30871}
}


title: MaDCoW: Marginal Distortion Correction for Wide-Angle Photography with Arbitrary Objects
authors: Kevin Zhang, Jia-Bin Huang, Jose Echevarria, Stephen DiVerdi, Aaron Hertzmann
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10923-10932
affiliations: ["UMD", "Adobe Research"]
cities: ["College Park", "San Jose"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_MaDCoW_Marginal_Distortion_Correction_for_Wide-Angle_Photography_with_Arbitrary_Objects_CVPR_2025_paper.html

abstract:
We introduce MaDCoW, a method for correcting marginal distortion of arbitrary objects in wide-angle photography. People often use wide-angle photography to convey natural scenes--smartphones typically default to wide-angle photography--but depicting very wide-field-of-view scenes produces distorted object appearance, particularly marginal distortion in linear projections. With MaDCoW, a user annotates regions-of-interest to correct, along with straight lines. For each region, MaDCoW solves for a local-linear perspective projection and then jointly solves for a projection for the whole photograph that minimizes distortion. We show that our method can produce good results in cases where previous methods yield visible distortions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_MaDCoW_Marginal_Distortion_Correction_for_Wide-Angle_Photography_with_Arbitrary_Objects_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_MaDCoW_Marginal_Distortion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Kevin and Huang, Jia-Bin and Echevarria, Jose and DiVerdi, Stephen and Hertzmann, Aaron},
    title     = {MaDCoW: Marginal Distortion Correction for Wide-Angle Photography with Arbitrary Objects},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10923-10932}
}

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


title: SynTab-LLaVA: Enhancing Multimodal Table Understanding with Decoupled Synthesis
authors: Bangbang Zhou, Zuan Gao, Zixiao Wang, Boqiang Zhang, Yuxin Wang, Zhineng Chen, Hongtao Xie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24796-24806
affiliations: ["University of Science and Technology of China", "Fudan Univeristy"]
cities: ["Hefei", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_SynTab-LLaVA_Enhancing_Multimodal_Table_Understanding_with_Decoupled_Synthesis_CVPR_2025_paper.html

abstract:
Due to the limited scale of multimodal table understanding (MTU) data, model performance is constrained. A straightforward approach is to use multimodal large language models to obtain more samples, but this may cause hallucinations, generate incorrect sample pairs, and cost significantly.To address the above issues, we design a simple yet effective synthesis framework that consists of two independent steps: table image rendering and table question and answer (Q&A) pairs generation.We use table codes (HTML, LaTeX, Markdown) to synthesize images and generate Q&A pairs with large language model (LLM).This approach leverages LLM's high concurrency and low cost to boost annotation efficiency and reduce expenses. By inputting code instead of images, LLMs can directly access the content and structure of the table, reducing hallucinations in table understanding and improving the accuracy of generated Q&A pairs. Finally, we synthesize a large-scale MTU dataset, SynTab, containing 636K images and 1.8M samples costing within \200 in US dollars. We further introduce a generalist tabular multimodal model, SynTab-LLaVA. This model not only effectively extracts local textual content within the table but also enables global modeling of relationships between cells.SynTab-LLaVA achieves SOTA performance on 21 out of 24 in-domain and out-of-domain benchmarks, demonstrating the effectiveness and generalization of our method. The Code is available at \href https://github.com/bang123-box/SynTab-LLaVA  SynTab-LLaVA .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_SynTab-LLaVA_Enhancing_Multimodal_Table_Understanding_with_Decoupled_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_SynTab-LLaVA_Enhancing_Multimodal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Bangbang and Gao, Zuan and Wang, Zixiao and Zhang, Boqiang and Wang, Yuxin and Chen, Zhineng and Xie, Hongtao},
    title     = {SynTab-LLaVA: Enhancing Multimodal Table Understanding with Decoupled Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24796-24806}
}

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


authors: Hanhui Wang, Yihua Zhang, Ruizheng Bai, Yue Zhao, Sijia Liu, Zhengzhong Tu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23806-23816
affiliations: ["University of Southern California", "Michigan State University", "Texas A&M University"]
cities: ["Los Angeles", "East Lansing", "College Station"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Edit_Away_and_My_Face_Will_not_Stay_Personal_Biometric_CVPR_2025_paper.html

title:
Edit Away and My Face Will not Stay: Personal Biometric Defense against Malicious Generative Editing

abstract:
Recent advancements in diffusion models have made generative image editing more accessible than ever. While these developments allow users to generate creative edits with ease, they also raise significant ethical concerns, particularly regarding malicious edits to human portraits that threaten individuals' privacy and identity security. Existing general-purpose image protection methods primarily focus on generating adversarial perturbations to nullify edit effects. However, these approaches often exhibit instability to protect against diverse editing requests. In this work, we introduce a novel perspective to personal human portrait protection against malicious editing. Unlike traditional methods aiming to prevent edits from taking effect, our method, FaceLock, optimizes adversarial perturbations to ensure that original biometric information---such as facial features---is either destroyed or substantially altered post-editing, rendering the subject in the edited output biometrically unrecognizable. Our approach innovatively integrates facial recognition and visual perception factors into the perturbation optimization process, ensuring robust protection against a variety of editing attempts. Besides, we shed light on several critical issues with commonly used evaluation metrics in image editing and reveal cheating methods by which they can be easily manipulated, leading to deceptive assessments of protection. Through extensive experiments, we demonstrate that FaceLock significantly outperforms all baselines in defense performance against a wide range of malicious edits. Moreover, our method also exhibits strong robustness against purification techniques. Comprehensive ablation studies confirm the stability and broad applicability of our method across diverse diffusion-based editing algorithms. Our work not only advances the state-of-the-art in biometric defense but also sets the foundation for more secure and privacy-preserving practices in image editing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Edit_Away_and_My_Face_Will_not_Stay_Personal_Biometric_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Edit_Away_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Hanhui and Zhang, Yihua and Bai, Ruizheng and Zhao, Yue and Liu, Sijia and Tu, Zhengzhong},
    title     = {Edit Away and My Face Will not Stay: Personal Biometric Defense against Malicious Generative Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23806-23816}
}

raw_affiliation:
[{"affiliation": "University of Southern California", "city": "Los Angeles", "country": "USA"}, {"affiliation": "Michigan State University", "city": "East Lansing", "country": "USA"}, {"affiliation": "Texas A&M University", "city": "College Station", "country": "USA"}]


title: Any6D: Model-free 6D Pose Estimation of Novel Objects
authors: Taeyeop Lee, Bowen Wen, Minjun Kang, Gyuree Kang, In So Kweon, Kuk-Jin Yoon
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11633-11643
affiliations: ["KAIST", "NVIDIA"]
cities: ["Daejeon", "Santa Clara"]
countries: ["South Korea", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Any6D_Model-free_6D_Pose_Estimation_of_Novel_Objects_CVPR_2025_paper.html

abstract:
We introduce Any6D, a model-free framework for 6D object pose estimation that requires only a single RGB-D anchor image to estimate both the 6D pose and size of unknown objects in novel scenes. Unlike existing methods that rely on textured 3D models or multiple viewpoints, Any6D leverages a joint object alignment process to enhance 2D-3D alignment and metric scale estimation for improved pose accuracy. Our approach integrates a render-and-compare strategy to generate and refine pose hypotheses, enabling robust performance in scenarios with occlusions, non-overlapping views, diverse lighting conditions, and large cross-environment variations. We evaluate our method on five challenging datasets: REAL275, Toyota-Light, HO3D, YCBINEOAT, and LM-O, demonstrating its effectiveness in significantly outperforming state-of-the-art methods for novel object pose estimation. Project page: https://taeyeop.com/any6d

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Any6D_Model-free_6D_Pose_Estimation_of_Novel_Objects_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Taeyeop and Wen, Bowen and Kang, Minjun and Kang, Gyuree and Kweon, In So and Yoon, Kuk-Jin},
    title     = {Any6D: Model-free 6D Pose Estimation of Novel Objects},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11633-11643}
}

raw_affiliation:
[{"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}]


title: Improving Accuracy and Calibration via Differentiated Deep Mutual Learning
authors: Han Liu, Peng Cui, Bingning Wang, Weipeng Chen, Yupeng Zhang, Jun Zhu, Xiaolin Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25812-25821
cities: ["Beijing", "Unknown", "Beijing", "Beijing"]
countries: ["China", "Unknown", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Improving_Accuracy_and_Calibration_via_Differentiated_Deep_Mutual_Learning_CVPR_2025_paper.html

abstract:
Deep Neural Networks (DNNs) have achieved remarkable success in a variety of tasks, particularly in terms of prediction accuracy. However, in real-world scenarios, especially in safety-critical applications, accuracy alone is insufficient; reliable uncertainty estimates are essential. Modern DNNs, often trained with cross-entropy loss, tend to exhibit overconfidence, especially on ambiguous samples. Many techniques aim to improve uncertainty calibration, yet they often come at the cost of reduced accuracy or increased computational demands. To address this challenge, we propose Differentiated Deep Mutual Learning (Diff-DML), an efficient ensemble approach that simultaneously enhances accuracy and uncertainty calibration. Diff-DML draws inspiration from Deep Mutual Learning (DML) while introducing two strategies to maintain prediction diversity: (1) Differentiated Training Strategy (DTS) and (2) Diversity-Preserving Learning Objective (DPLO). Our theoretical analysis shows that Diff-DML's diversified learning framework not only leverages ensemble benefits but also avoids the loss of prediction diversity observed in traditional DML setups, which is crucial for improved calibration. Extensive evaluations on various benchmarks confirm the effectiveness of Diff-DML. For instance, on the CIFAR-100 dataset, Diff-DML on ResNet34 model achieved substantial improvements over the previous state-of-the-art method, MDCA, with absolute accuracy gains of 1.3%/3.1%, relative ECE reductions of 49.6%/43.8%, and relative classwise-ECE reductions of 7.7%/13.0%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Improving_Accuracy_and_Calibration_via_Differentiated_Deep_Mutual_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Improving_Accuracy_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Han and Cui, Peng and Wang, Bingning and Chen, Weipeng and Zhang, Yupeng and Zhu, Jun and Hu, Xiaolin},
    title     = {Improving Accuracy and Calibration via Differentiated Deep Mutual Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25812-25821}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science and Technology, Institute for AI, BNRist, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Baichuan Inc.", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Tsinghua Laboratory of Brain and Intelligence (THBI), IDG/McGovern Institute for Brain Research, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Chinese Institute for Brain Research (CIBR)", "city": "Beijing", "country": "China"}]

affiliations:
["Department of Computer Science and Technology, Institute for AI, BNRist, Tsinghua University", "Baichuan Inc.", "Tsinghua Laboratory of Brain and Intelligence (THBI), IDG/McGovern Institute for Brain Research, Tsinghua University", "Chinese Institute for Brain Research (CIBR)"]


title: DrVideo: Document Retrieval Based Long Video Understanding
authors: Ziyu Ma, Chenhui Gou, Hengcan Shi, Bin Sun, Shutao Li, Hamid Rezatofighi, Jianfei Cai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18936-18946
cities: ["Changsha", "Melbourne"]
countries: ["China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_DrVideo_Document_Retrieval_Based_Long_Video_Understanding_CVPR_2025_paper.html

abstract:
Most of the existing methods for video understanding primarily focus on videos only lasting tens of seconds, with limited exploration of techniques for handling long videos. The increased number of frames in long videos poses two main challenges: difficulty in locating key information and performing long-range reasoning. Thus, we propose DrVideo, a document-retrieval-based system designed for long video understanding. Our key idea is to convert the long-video understanding problem into a long-document understanding task so as to effectively leverage the power of large language models. Specifically, DrVideo first transforms a long video into a coarse text-based long document to initially retrieve key frames and then updates the documents with the augmented key frame information. It then employs an agent-based iterative loop to continuously search for missing information and augment the document until sufficient question-related information is gathered for making the final predictions in a chain-of-thought manner. Extensive experiments on long video benchmarks confirm the effectiveness of our method. DrVideo significantly outperforms existing LLM-based state-of-the-art methods on EgoSchema benchmark (3 minutes), MovieChat-1K benchmark (10 minutes), and the long split of Video-MME benchmark (average of 44 minutes). Code is available at https://github.com/Upper9527/DrVideo.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_DrVideo_Document_Retrieval_Based_Long_Video_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_DrVideo_Document_Retrieval_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Ziyu and Gou, Chenhui and Shi, Hengcan and Sun, Bin and Li, Shutao and Rezatofighi, Hamid and Cai, Jianfei},
    title     = {DrVideo: Document Retrieval Based Long Video Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18936-18946}
}

raw_affiliation:
[{"affiliation": "College of Electrical and Information Engineering, Hunan University", "city": "Changsha", "country": "China"}, {"affiliation": "Data Science & AI Department, Faculty of IT, Monash University", "city": "Melbourne", "country": "Australia"}]

affiliations:
["College of Electrical and Information Engineering, Hunan University", "Data Science & AI Department, Faculty of IT, Monash University"]


title: Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning
authors: Ye Li, Yanchao Zhao, Chengcheng Zhu, Jiale Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25770-25779
affiliations: ["Nanjing University of Aeronautics and Astronautics", "Yangzhou University"]
cities: ["Nanjing", "Yangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Infighting_in_the_Dark_Multi-Label_Backdoor_Attack_in_Federated_Learning_CVPR_2025_paper.html

abstract:
Federated Learning (FL), a privacy-preserving decentralized machine learning framework, has been shown to be vulnerable to backdoor attacks. Current research primarily focuses on the Single-Label Backdoor Attack (SBA), wherein adversaries share a consistent target. However, a critical fact is overlooked: adversaries may be non-cooperative, have distinct targets, and operate independently, which exhibits a more practical scenario called Multi-Label Backdoor Attack (MBA). Unfortunately, prior works are ineffective in the MBA scenario since non-cooperative attackers exclude each other. In this work, we conduct an in-depth investigation to uncover the inherent constraints of the exclusion: similar backdoor mappings are constructed for different targets, resulting in conflicts among backdoor functions. To address this limitation, we propose Mirage, the first non-cooperative MBA strategy in FL that allows attackers to inject effective and persistent backdoors into the global model without collusion by constructing in-distribution (ID) backdoor mapping. Specifically, we introduce an adversarial adaptation method to bridge the backdoor features and the target distribution in an ID manner. Additionally, we further leverage a constrained optimization method to ensure the ID mapping survives in the global training dynamics. Extensive evaluations demonstrate that Mirage outperforms various state-of-the-art attacks and bypasses existing defenses, achieving an average ASR greater than 97% and maintaining over 90% after 900 rounds. This work aims to alert researchers to this potential threat and inspire the design of effective defense mechanisms. Code has been made open-source.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Infighting_in_the_Dark_Multi-Label_Backdoor_Attack_in_Federated_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Infighting_in_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Ye and Zhao, Yanchao and Zhu, Chengcheng and Zhang, Jiale},
    title     = {Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25770-25779}
}

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


title: Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17660-17670
affiliations: ["Stanford University", "Massachusetts Institute of Technology", "Adobe Research"]
cities: ["Stanford", "Cambridge", "San Jose"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kuang_Buffer_Anytime_Zero-Shot_Video_Depth_and_Normal_from_Image_Priors_CVPR_2025_paper.html

authors:
Zhengfei Kuang, Tianyuan Zhang, Kai Zhang, Hao Tan, Sai Bi, Yiwei Hu, Zexiang Xu, Milos Hasan, Gordon Wetzstein, Fujun Luan

abstract:
We present Buffer Anytime, a framework for estimation of depth and normal maps (which we call geometric buffers) from video that eliminates the need for paired video--depth and video--normal training data. Instead of relying on large-scale annotated video datasets, we demonstrate high-quality video buffer estimation by leveraging single-image priors with temporal consistency constraints. Our zero-shot training strategy combines state-of-the-art image estimation models based on optical flow smoothness through a hybrid loss function, implemented via a lightweight temporal attention architecture. Applied to leading image models like Depth Anything V2 and Marigold-E2E-FT, our approach significantly improves temporal consistency while maintaining accuracy. Experiments show that our method not only outperforms image-based approaches but also achieves results comparable to state-of-the-art video models trained on large-scale paired video datasets, despite using no such paired video data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kuang_Buffer_Anytime_Zero-Shot_Video_Depth_and_Normal_from_Image_Priors_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kuang_Buffer_Anytime_Zero-Shot_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kuang_2025_CVPR,
    author    = {Kuang, Zhengfei and Zhang, Tianyuan and Zhang, Kai and Tan, Hao and Bi, Sai and Hu, Yiwei and Xu, Zexiang and Hasan, Milos and Wetzstein, Gordon and Luan, Fujun},
    title     = {Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17660-17670}
}

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


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16008-16018
affiliations: ["HKUST", "Southeast University", "Tsinghua University", "V AST"]
cities: ["Hong Kong", "Nanjing", "Beijing", "Unknown"]
countries: ["China", "China", "China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_PSHuman_Photorealistic_Single-image_3D_Human_Reconstruction_using_Cross-Scale_Multiview_Diffusion_CVPR_2025_paper.html

title:
PSHuman: Photorealistic Single-image 3D Human Reconstruction using Cross-Scale Multiview Diffusion and Explicit Remeshing

authors:
Peng Li, Wangguandong Zheng, Yuan Liu, Tao Yu, Yangguang Li, Xingqun Qi, Xiaowei Chi, Siyu Xia, Yan-Pei Cao, Wei Xue, Wenhan Luo, Yike Guo

abstract:
Photorealistic 3D human modeling is essential for various applications and has seen tremendous progress. However, existing methods for monocular full-body reconstruction, typically relying on front and/or predicted back view, still struggle with satisfactory performance due to the ill-posed nature of the problem and sophisticated self-occlusions. In this paper, we propose PSHuman, a novel framework that explicitly reconstructs human meshes utilizing priors from the multiview diffusion model. It is found that directly applying multiview diffusion on single-view human images  leads to severe geometric distortions, especially on generated faces. To address it, we propose a cross-scale diffusion that models the joint probability distribution of global full-body shape and local facial characteristics, enabling identity-preserved novel-view generation without geometric distortion. Moreover, to enhance cross-view body shape consistency of varied human poses, we condition the generative model on parametric models (SMPL-X), which provide body priors and prevent unnatural views inconsistent with human anatomy. Leveraging the generated multiview normal and color images, we present SMPLX-initialized explicit human carving to recover realistic textured human meshes efficiently. Extensive experiments on CAPE and THuman2.1 demonstrate PSHuman's superiority in geometry details, texture fidelity, and generalization capability.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_PSHuman_Photorealistic_Single-image_3D_Human_Reconstruction_using_Cross-Scale_Multiview_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_PSHuman_Photorealistic_Single-image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Peng and Zheng, Wangguandong and Liu, Yuan and Yu, Tao and Li, Yangguang and Qi, Xingqun and Chi, Xiaowei and Xia, Siyu and Cao, Yan-Pei and Xue, Wei and Luo, Wenhan and Guo, Yike},
    title     = {PSHuman: Photorealistic Single-image 3D Human Reconstruction using Cross-Scale Multiview Diffusion and Explicit Remeshing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16008-16018}
}

raw_affiliation:
[{"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "V AST", "city": "Unknown", "country": "Unknown"}]


title: LSNet: See Large, Focus Small
authors: Ao Wang, Hui Chen, Zijia Lin, Jungong Han, Guiguang Ding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9718-9729
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_LSNet_See_Large_Focus_Small_CVPR_2025_paper.html

abstract:
Vision network designs, including Convolutional Neural Networks and Vision Transformers, have significantly advanced the field of computer vision. Yet, their complex computations pose challenges for practical deployments, particularly in real-time applications. To tackle this issue, researchers have explored various lightweight and efficient network designs. However, existing lightweight models predominantly leverage self-attention mechanisms and convolutions for token mixing. This dependence brings limitations in effectiveness and efficiency in the perception and aggregation processes of lightweight networks, hindering the balance between performance and efficiency under limited computational budgets. In this paper, we draw inspiration from the dynamic heteroscale vision ability inherent in the efficient human vision system and propose a "See Large, Focus Small" strategy for lightweight vision network design. We introduce LS (Large-Small) convolution, which combines large-kernel perception and small-kernel aggregation. It can efficiently capture a wide range of perceptual information and achieve precise feature aggregation for dynamic and complex visual representations, thus enabling proficient processing of visual information. Based on LS convolution, we present LSNet, a new family of lightweight models. Extensive experiments demonstrate that LSNet achieves superior performance and efficiency over existing lightweight networks in various vision tasks. Codes and models are available at https://github.com/jameslahm/lsnet.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_LSNet_See_Large_Focus_Small_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_LSNet_See_Large_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Ao and Chen, Hui and Lin, Zijia and Han, Jungong and Ding, Guiguang},
    title     = {LSNet: See Large, Focus Small},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9718-9729}
}

raw_affiliation:
[{"affiliation": "School of Software, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "BNRist, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Department of Automation, Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["School of Software, Tsinghua University", "BNRist, Tsinghua University", "Department of Automation, Tsinghua University"]


title: DynamicScaler: Seamless and Scalable Video Generation for Panoramic Scenes
authors: Jinxiu Liu, Shaoheng Lin, Yinxiao Li, Ming-Hsuan Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6144-6153
affiliations: ["South China University of Technology", "Google DeepMind", "UC Merced"]
cities: ["Guangzhou", "Mountain View", "Merced"]
countries: ["China", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_DynamicScaler_Seamless_and_Scalable_Video_Generation_for_Panoramic_Scenes_CVPR_2025_paper.html

abstract:
The increasing demand for immersive AR/VR applications and spatial intelligence has heightened the need to generate high-quality scene-level and 360deg panoramic video. However, most video diffusion models are constrained by limited resolution and aspect ratio, which restricts their applicability to scene-level dynamic content synthesis. In this work, we propose DynamicScaler, addressing these challenges by enabling spatially scalable and panoramic dynamic scene synthesis that preserves coherence across panoramic scenes of arbitrary size. Specifically, we introduce a Offset Shifting Denoiser, facilitating efficient, synchronous, and coherent denoising panoramic dynamic scenes via a diffusion model with fixed resolution through a seamless rotating Window, which ensures seamless boundary transitions and consistency across the entire panoramic space, accommodating varying resolutions and aspect ratios. Additionally, we employ a Global Motion Guidance mechanism to ensure both local detail fidelity and global motion continuity. Extensive experiments demonstrate our method achieves superior content and motion quality in panoramic scene-level video generation, offering a training-free, efficient, and scalable solution for immersive dynamic scene creation with constant VRAM consumption regardless of the output video resolution. Project page is available at https://dynamic-scaler.pages.dev/new.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_DynamicScaler_Seamless_and_Scalable_Video_Generation_for_Panoramic_Scenes_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Jinxiu and Lin, Shaoheng and Li, Yinxiao and Yang, Ming-Hsuan},
    title     = {DynamicScaler: Seamless and Scalable Video Generation for Panoramic Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6144-6153}
}

raw_affiliation:
[{"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}, {"affiliation": "UC Merced", "city": "Merced", "country": "USA"}]


title: Tartan IMU: A Light Foundation Model for Inertial Positioning in Robotics
authors: Shibo Zhao, Sifan Zhou, Raphael Blanchard, Yuheng Qiu, Wenshan Wang, Sebastian Scherer
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22520-22529
raw_affiliation: [{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]
affiliations: ["Carnegie Mellon University"]
cities: ["Pittsburgh"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Tartan_IMU_A_Light_Foundation_Model_for_Inertial_Positioning_in_CVPR_2025_paper.html

abstract:
Despite recent advances in deep learning, most existing learning IMU odometry methods are trained on specific datasets, lack generalization, and are prone to overfitting, which limits their real-world application. To address these challenges, we present Tartan IMU, a foundation model designed for generalizable, IMU-based state estimation across diverse robotic platforms. Our approach consists of three-stage: First, a pre-trained foundation model leverages over 100 hours of multi-platform data to establish general motion knowledge, achieving 36% improvement in ATE over specialized models. Second, to adapt to previously unseen tasks, we employ the Low-Rank Adaptation (LoRA), allowing positive transfer with only 1.1 M trainable parameters. Finally, to support robotics deployment, we introduce online test-time adaptation, which eliminates the boundary between training and testing, allowing the model to continuously "learn as it operates" at 200 FPS in real-time.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Tartan_IMU_A_Light_Foundation_Model_for_Inertial_Positioning_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Tartan_IMU_A_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Shibo and Zhou, Sifan and Blanchard, Raphael and Qiu, Yuheng and Wang, Wenshan and Scherer, Sebastian},
    title     = {Tartan IMU: A Light Foundation Model for Inertial Positioning in Robotics},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22520-22529}
}


title: Event Ellipsometer: Event-based Mueller-Matrix Video Imaging
authors: Ryota Maeda, Yunseong Moon, Seung-Hwan Baek
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21804-21813
affiliations: ["POSTECH", "University of Hyogo"]
cities: ["Pohang", "Hyogo"]
countries: ["South Korea", "Japan"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Maeda_Event_Ellipsometer_Event-based_Mueller-Matrix_Video_Imaging_CVPR_2025_paper.html

abstract:
Light-matter interactions modify both the intensity and polarization state of light. Changes in polarization, represented by a Mueller matrix, encode detailed scene information. Existing optical ellipsometers capture Mueller-matrix images; however, they are often limited to static scenes due to long acquisition times. Here, we introduce Event Ellipsometer, a method for acquiring Mueller-matrix images of dynamic scenes. Our imaging system employs fast-rotating quarter-wave plates (QWPs) in front of a light source and an event camera that asynchronously captures intensity changes induced by the rotating QWPs. We develop an ellipsometric-event image formation model, a calibration method, and an ellipsometric-event reconstruction method. We experimentally demonstrate that Event Ellipsometer enables Mueller-matrix imaging at 30fps, extending ellipsometry to dynamic scenes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Maeda_Event_Ellipsometer_Event-based_Mueller-Matrix_Video_Imaging_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Maeda_Event_Ellipsometer_Event-based_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Maeda_2025_CVPR,
    author    = {Maeda, Ryota and Moon, Yunseong and Baek, Seung-Hwan},
    title     = {Event Ellipsometer: Event-based Mueller-Matrix Video Imaging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21804-21813}
}

raw_affiliation:
[{"affiliation": "POSTECH", "city": "Pohang", "country": "South Korea"}, {"affiliation": "University of Hyogo", "city": "Hyogo", "country": "Japan"}]


authors: Wenhui Liao, Jiapeng Wang, Hongliang Li, Chengyu Wang, Jun Huang, Lianwen Jin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4038-4049
cities: ["Guangzhou", "Hangzhou", "Zhuhai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liao_DocLayLLM_An_Efficient_Multi-modal_Extension_of_Large_Language_Models_for_CVPR_2025_paper.html

title:
DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understanding

abstract:
Text-rich document understanding (TDU) requires comprehensive analysis of documents containing substantial textual content and complex layouts. While Multimodal Large Language Models (MLLMs) have achieved fast progress in this domain, existing approaches either demand significant computational resources or struggle with effective multi-modal integration. In this paper, we introduce DocLayLLM, an efficient multi-modal extension of LLMs specifically designed for TDU. By lightly integrating visual patch tokens and 2D positional tokens into LLMs' input and encoding the document content using the LLMs themselves, we fully take advantage of the document comprehension capability of LLMs and enhance their perception of OCR information. We have also deeply considered the role of chain-of-thought (CoT) and innovatively proposed the techniques of CoT Pre-training and CoT Annealing. Our DocLayLLM can achieve remarkable performances with lightweight training settings, showcasing its efficiency and effectiveness. Experimental results demonstrate that our DocLayLLM outperforms existing OCR-dependent methods and OCR-free competitors. Code and model are available at https://github.com/whlscut/DocLayLLM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liao_DocLayLLM_An_Efficient_Multi-modal_Extension_of_Large_Language_Models_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liao_DocLayLLM_An_Efficient_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liao_2025_CVPR,
    author    = {Liao, Wenhui and Wang, Jiapeng and Li, Hongliang and Wang, Chengyu and Huang, Jun and Jin, Lianwen},
    title     = {DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4038-4049}
}

raw_affiliation:
[{"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Alibaba Cloud Computing", "city": "Hangzhou", "country": "China"}, {"affiliation": "SCUT-Zhuhai Institute of Modern Industrial Innovation", "city": "Zhuhai", "country": "China"}]

affiliations:
["South China University of Technology", "Alibaba Cloud Computing", "SCUT-Zhuhai Institute of Modern Industrial Innovation"]


title: EDEN: Enhanced Diffusion for High-quality Large-motion Video Frame Interpolation
authors: Zihao Zhang, Haoran Chen, Haoyu Zhao, Guansong Lu, Yanwei Fu, Hang Xu, Zuxuan Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2105-2115
cities: ["Shanghai", "Shanghai", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_EDEN_Enhanced_Diffusion_for_High-quality_Large-motion_Video_Frame_Interpolation_CVPR_2025_paper.html

abstract:
Handling complex or nonlinear motion patterns has long posed challenges for video frame interpolation. Although recent advances in diffusion-based methods offer improvements over traditional optical flow-based approaches, they still struggle to generate sharp, temporally consistent frames in scenarios with large motion. To address this limitation, we introduce EDEN, an Enhanced Diffusion for high-quality large-motion vidEo frame iNterpolation. Our approach first utilizes a transformer-based tokenizer to produce refined latent representations of the intermediate frames for diffusion models. We then enhance the diffusion transformer with temporal attention across the process and incorporate a start-end frame difference embedding to guide the generation of dynamic motion. Extensive experiments demonstrate that EDEN achieves state-of-the-art results across popular benchmarks, including nearly a 10% LPIPS reduction on DAVIS and SNU-FILM, and an 8% improvement on DAIN-HD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_EDEN_Enhanced_Diffusion_for_High-quality_Large-motion_Video_Frame_Interpolation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_EDEN_Enhanced_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zihao and Chen, Haoran and Zhao, Haoyu and Lu, Guansong and Fu, Yanwei and Xu, Hang and Wu, Zuxuan},
    title     = {EDEN: Enhanced Diffusion for High-quality Large-motion Video Frame Interpolation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2105-2115}
}

raw_affiliation:
[{"affiliation": "Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Collaborative Innovation Center of Intelligent Visual Computing", "city": "Shanghai", "country": "China"}, {"affiliation": "Noah\u2019s Ark Lab, Huawei", "city": "Shenzhen", "country": "China"}]

affiliations:
["Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University", "Shanghai Collaborative Innovation Center of Intelligent Visual Computing", "Noah\u2019s Ark Lab, Huawei"]


title: Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor
authors: Hao Yu, Xin Yang, Le Zhang, Hanlin Gu, Tianrui Li, Lixin Fan, Qiang Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4874-4883
cities: ["Chengdu", "Chengdu", "Chengdu", "Shenzhen", "Chengdu", "Shenzhen", "Hong Kong"]
countries: ["China", "China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Handling_Spatial-Temporal_Data_Heterogeneity_for_Federated_Continual_Learning_via_Tail_CVPR_2025_paper.html

abstract:
Federated Continual Learning (FCL) allows each client to continually update its knowledge from task streams, enhancing the applicability of federated learning in real-world scenarios. However, FCL needs to address not only spatial data heterogeneity between clients but also temporal data heterogeneity between tasks. In this paper, empirical experiments demonstrate that such input-level heterogeneity significantly affects the model's internal parameters and outputs, leading to severe spatial-temporal catastrophic forgetting of local and previous knowledge. To this end, we propose Federated Tail Anchor (FedTA) to mix trainable Tail Anchor with the frozen output features to adjust their position in the feature space, thereby overcoming parameter-forgetting and output-forgetting. Three novel components are also included: Input Enhancement for improving the performance of pre-trained models on downstream tasks; Selective Input Knowledge Fusion for fusion of heterogeneous local knowledge on the server; and Best Global Prototype Selection for finding the best anchor point for each class in the feature space. Extensive experiments demonstrate that FedTA not only outperforms existing FCL methods but also effectively preserves the relative positions of features.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Handling_Spatial-Temporal_Data_Heterogeneity_for_Federated_Continual_Learning_via_Tail_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_Handling_Spatial-Temporal_Data_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Hao and Yang, Xin and Zhang, Le and Gu, Hanlin and Li, Tianrui and Fan, Lixin and Yang, Qiang},
    title     = {Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4874-4883}
}

raw_affiliation:
[{"affiliation": "Southwestern University of Finance and Economics", "city": "Chengdu", "country": "China"}, {"affiliation": "Southwestern University of Finance and Economics", "city": "Chengdu", "country": "China"}, {"affiliation": "University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}, {"affiliation": "WeBank", "city": "Shenzhen", "country": "China"}, {"affiliation": "Southwest Jiaotong University", "city": "Chengdu", "country": "China"}, {"affiliation": "WeBank", "city": "Shenzhen", "country": "China"}, {"affiliation": "Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}]

affiliations:
["Southwestern University of Finance and Economics", "Southwestern University of Finance and Economics", "University of Electronic Science and Technology of China", "WeBank", "Southwest Jiaotong University", "WeBank", "Hong Kong University of Science and Technology"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6782-6791
raw_affiliation: [{"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}]
affiliations: ["UC Berkeley"]
cities: ["Berkeley"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Peng_DeSiRe-GS_4D_Street_Gaussians_for_Static-Dynamic_Decomposition_and_Surface_Reconstruction_CVPR_2025_paper.html

title:
DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes

authors:
Chensheng Peng, Chengwei Zhang, Yixiao Wang, Chenfeng Xu, Yichen Xie, Wenzhao Zheng, Kurt Keutzer, Masayoshi Tomizuka, Wei Zhan

abstract:
We present DeSiRe-GS, a self-supervised gaussian splatting representation, enabling effective static-dynamic decomposition and high-fidelity surface reconstruction in complex driving scenarios. Our approach employs a two-stage optimization pipeline of dynamic street Gaussians. In the first stage, we extract 2D motion masks based on the observation that 3D Gaussian Splatting inherently can  reconstruct only the static regions in dynamic environments. These extracted 2D motion priors are then mapped into the Gaussian space in a differentiable manner, leveraging an efficient formulation of dynamic Gaussians in the second stage. Combined with the introduced geometric regularizations, our method are able to address the over-fitting issues caused by data sparsity in autonomous driving, reconstructing physically plausible Gaussians that align with object surfaces  rather than floating in air. Furthermore, we introduce  temporal cross-view consistency to ensure coherence across time and viewpoints, resulting in high-quality surface reconstruction. Comprehensive experiments demonstrate the efficiency and effectiveness of DeSiRe-GS, surpassing prior self-supervised arts and achieving accuracy comparable to methods relying on external 3D bounding box annotations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Peng_DeSiRe-GS_4D_Street_Gaussians_for_Static-Dynamic_Decomposition_and_Surface_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Peng_DeSiRe-GS_4D_Street_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Peng_2025_CVPR,
    author    = {Peng, Chensheng and Zhang, Chengwei and Wang, Yixiao and Xu, Chenfeng and Xie, Yichen and Zheng, Wenzhao and Keutzer, Kurt and Tomizuka, Masayoshi and Zhan, Wei},
    title     = {DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6782-6791}
}


title: End-to-End HOI Reconstruction Transformer with Graph-based Encoding
authors: Zhenrong Wang, Qi Zheng, Sihan Ma, Maosheng Ye, Yibing Zhan, Dongjiang Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27706-27715
affiliations: ["Shenzhen University", "University of Sydney", "DeepRoute.AI", "JD Explore Academy"]
cities: ["Shenzhen", "Sydney", "Beijing", "Beijing"]
countries: ["China", "Australia", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_End-to-End_HOI_Reconstruction_Transformer_with_Graph-based_Encoding_CVPR_2025_paper.html

abstract:
Human-object interaction (HOI) reconstruction has garnered significant attention due to its diverse applications and the success of capturing human meshes. Existing HOI reconstruction methods often rely on explicitly modeling interactions between humans and objects. However, such a way leads to a natural conflict between 3D mesh reconstruction, which emphasizes global structure, and fine-grained contact reconstruction, which focuses on local details. To address the limitations of explicit modeling, we propose the End-to-End HOI Reconstruction Transformer with Graph-based Encoding (HOI-TG). It implicitly learns the interaction between humans and objects by leveraging self-attention mechanisms. Within the transformer architecture, we devise graph residual blocks to aggregate the topology among vertices of different spatial structures. This dual focus effectively balances global and local representations. Without bells and whistles, HOI-TG achieves state-of-the-art performance on BEHAVE and InterCap datasets. Particularly on the challenging InterCap dataset, our method improves the reconstruction results for human and object meshes by 8.9% and 8.6%, respectively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_End-to-End_HOI_Reconstruction_Transformer_with_Graph-based_Encoding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_End-to-End_HOI_Reconstruction_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zhenrong and Zheng, Qi and Ma, Sihan and Ye, Maosheng and Zhan, Yibing and Li, Dongjiang},
    title     = {End-to-End HOI Reconstruction Transformer with Graph-based Encoding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27706-27715}
}

raw_affiliation:
[{"affiliation": "Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "University of Sydney", "city": "Sydney", "country": "Australia"}, {"affiliation": "DeepRoute.AI", "city": "Beijing", "country": "China"}, {"affiliation": "JD Explore Academy", "city": "Beijing", "country": "China"}]


title: REWIND: Real-Time Egocentric Whole-Body Motion Diffusion with Exemplar-Based Identity Conditioning
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7095-7104
affiliations: ["Codec Avatars Lab, Meta", "KAIST", "Imperial College London"]
cities: ["Menlo Park", "Daejeon", "London"]
countries: ["USA", "South Korea", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_REWIND_Real-Time_Egocentric_Whole-Body_Motion_Diffusion_with_Exemplar-Based_Identity_Conditioning_CVPR_2025_paper.html

authors:
Jihyun Lee, Weipeng Xu, Alexander Richard, Shih-En Wei, Shunsuke Saito, Shaojie Bai, Te-Li Wang, Minhyuk Sung, Tae-Kyun Kim, Jason Saragih

abstract:
We present REWIND (Real-Time Egocentric Whole-Body Motion Diffusion), a one-step diffusion model for real-time, high-fidelity human motion estimation from egocentric image inputs. While an existing method for egocentric whole-body (i.e., body and hands) motion estimation is non-real-time and acausal due to diffusion-based iterative motion refinement to capture correlations between body and hand poses, REWIND operates in a fully causal and real-time manner. To enable real-time inference, we introduce (1) cascaded body-hand denoising diffusion, which effectively models the correlation between egocentric body and hand motions in a fast, feed-forward manner, and (2) diffusion distillation, which enables high-quality motion estimation with a single denoising step. Our denoising diffusion model is based on a modified Transformer architecture, designed to causally model output motions while enhancing generalizability to unseen motion lengths. Additionally, REWIND optionally supports identity-conditioned motion estimation when identity prior is available. To this end, we propose a novel identity conditioning method based on a small set of pose exemplars of the target identity, which further enhances motion estimation quality. Through extensive experiments, we demonstrate that REWIND significantly outperforms the existing baselines both with and without exemplar-based identity conditioning.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_REWIND_Real-Time_Egocentric_Whole-Body_Motion_Diffusion_with_Exemplar-Based_Identity_Conditioning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_REWIND_Real-Time_Egocentric_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Jihyun and Xu, Weipeng and Richard, Alexander and Wei, Shih-En and Saito, Shunsuke and Bai, Shaojie and Wang, Te-Li and Sung, Minhyuk and Kim, Tae-Kyun and Saragih, Jason},
    title     = {REWIND: Real-Time Egocentric Whole-Body Motion Diffusion with Exemplar-Based Identity Conditioning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7095-7104}
}

raw_affiliation:
[{"affiliation": "Codec Avatars Lab, Meta", "city": "Menlo Park", "country": "USA"}, {"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Imperial College London", "city": "London", "country": "UK"}]


