# papers

title: Generative Gaussian Splatting for Unbounded 3D City Generation
authors: Haozhe Xie, Zhaoxi Chen, Fangzhou Hong, Ziwei Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6111-6120
affiliations: ["S-Lab, Nanyang Technological University"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Generative_Gaussian_Splatting_for_Unbounded_3D_City_Generation_CVPR_2025_paper.html

abstract:
3D city generation with NeRF-based methods shows promising generation results but is computationally inefficient. Recently 3D Gaussian splatting (3D-GS) has emerged as a highly efficient alternative for object-level 3D generation. However, adapting 3D-GS from finite-scale 3D objects and humans to infinite-scale 3D cities is non-trivial. Unbounded 3D city generation entails significant storage overhead (out-of-memory issues), arising from the need to expand points to billions, often demanding hundreds of Gigabytes of VRAM for a city scene spanning 10km^2. In this paper, we propose **GaussianCity**, a generative Gaussian splatting framework dedicated to efficiently synthesizing unbounded 3D cities with a single feed-forward pass. Our key insights are two-fold: **1)** *Compact 3D Scene Representation*: We introduce BEV-Point as a highly compact intermediate representation, ensuring that the growth in VRAM usage for unbounded scenes remains constant, thus enabling unbounded city generation. **2)** *Spatial-aware Gaussian Attribute Decoder*: We present spatial-aware BEV-Point decoder to produce 3D Gaussian attributes, which leverages Point Serializer to integrate the structural and contextual characteristics of BEV points. Extensive experiments demonstrate that GaussianCity achieves state-of-the-art results in both drone-view and street-view 3D city generation.  Notably, compared to CityDreamer, GaussianCity exhibits superior performance with a speedup of 60 times (10.72 FPS v.s. 0.18 FPS).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Generative_Gaussian_Splatting_for_Unbounded_3D_City_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_Generative_Gaussian_Splatting_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Haozhe and Chen, Zhaoxi and Hong, Fangzhou and Liu, Ziwei},
    title     = {Generative Gaussian Splatting for Unbounded 3D City Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6111-6120}
}

raw_affiliation:
[{"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]


title: SVLTA: Benchmarking Vision-Language Temporal Alignment via Synthetic Video Situation
authors: Hao Du, Bo Wu, Yan Lu, Zhendong Mao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13798-13809
cities: ["Hefei", "Cambridge", "Hong Kong"]
countries: ["China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Du_SVLTA_Benchmarking_Vision-Language_Temporal_Alignment_via_Synthetic_Video_Situation_CVPR_2025_paper.html

abstract:
Vision-language temporal alignment is a crucial capability for human dynamic recognition and cognition in real-world scenarios. While existing research focuses on capturing vision-language relevance, it faces limitations due to biased temporal distributions, imprecise annotations, and insufficient compositionally. To achieve fair evaluation and comprehensive exploration, our objective is to investigate and evaluate the ability of models to achieve alignment from a temporal perspective, specifically focusing on their capacity to synchronize visual scenarios with linguistic context in a temporally coherent manner. As a preliminary step, we present the statistical analysis of existing benchmarks and reveal the existing challenges from a decomposed perspective. To this end, we introduce SVLTA, the synthetic vision-language temporal alignment derived via a well-designed and feasible control generation method within a simulation environment. The approach considers commonsense knowledge, manipulable action, and constrained filtering, which generates reasonable, diverse, and balanced data distributions for diagnostic evaluations. Our experiments reveal diagnostic insights through the evaluations in temporal question answering, distributional shift sensitiveness, and temporal alignment adaptation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Du_SVLTA_Benchmarking_Vision-Language_Temporal_Alignment_via_Synthetic_Video_Situation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Du_SVLTA_Benchmarking_Vision-Language_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Du_2025_CVPR,
    author    = {Du, Hao and Wu, Bo and Lu, Yan and Mao, Zhendong},
    title     = {SVLTA: Benchmarking Vision-Language Temporal Alignment via Synthetic Video Situation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13798-13809}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "MIT-IBM Watson AI Lab", "city": "Cambridge", "country": "USA"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["University of Science and Technology of China", "MIT-IBM Watson AI Lab", "The Chinese University of Hong Kong"]


title: Mixture of Submodules for Domain Adaptive Person Search
authors: Minsu Kim, Seungryong Kim, Kwanghoon Sohn
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13990-14001
affiliations: ["Samsung Electronics", "KAIST", "Yonsei University"]
cities: ["Seoul", "Daejeon", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Mixture_of_Submodules_for_Domain_Adaptive_Person_Search_CVPR_2025_paper.html

abstract:
Existing technique on domain adaptive person search commonly utilizes the unified framework for jointly localizing and identifying the person across domains. This framework, however, inevitably results in the gradient conflict problem, particularly in cross-domain scenarios with contradictory objectives, as the unified framework employs shared parameters to simultaneously address person detection and re-identification tasks across the domains. To overcome this, we present a novel mixture of submodules framework, dubbed MoS, that dynamically modulates the combination of submodules depending on the specific task to perform person detection and re-identification, separately. We further design the mixtures of submodules that vary depending on the domain, enabling domain-specific knowledge transfer. Especially, we decompose the main model into several submodules and employ diverse mixtures of submodules that vary depending on the tasks and domains through the conditional routing policy. In addition, we also present counterpart domain sample generation that synthesizes the augmented sample and uses them to learn domain invariant representation for person re-identification through the contrastive domain alignment. We conduct experiments to demonstrate the effectiveness of our MoS over the existing domain adaptive person search method and provide ablation studies.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Mixture_of_Submodules_for_Domain_Adaptive_Person_Search_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Mixture_of_Submodules_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Minsu and Kim, Seungryong and Sohn, Kwanghoon},
    title     = {Mixture of Submodules for Domain Adaptive Person Search},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13990-14001}
}

raw_affiliation:
[{"affiliation": "Samsung Electronics", "city": "Seoul", "country": "South Korea"}, {"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Yonsei University", "city": "Seoul", "country": "South Korea"}]


title: Unsupervised Discovery of Facial Landmarks and Head Pose
authors: Satyajit Tourani, Siddharth Tourani, Arif Mahmood, Muhammad Haris Khan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21192-21202
cities: ["Hyderabad", "Abu Dhabi", "Heidelberg", "Lahore"]
countries: ["India", "UAE", "Germany", "Pakistan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tourani_Unsupervised_Discovery_of_Facial_Landmarks_and_Head_Pose_CVPR_2025_paper.html

abstract:
Unsupervised landmark and head pose estimation is fundamental in fields like biometrics, augmented reality, and emotion recognition, offering accurate spatial data without relying on labeled datasets. It enhances scalability, adaptability, and generalization across diverse settings, where manual labeling is costly. In this work we exploit Stable Diffusion to approach the challenging problem of unsupervised landmarks and head pose estimation and make following contributions. (a) We propose a semantic-aware landmark localization algorithm including a consistent landmarks selection technique. (b) To encode landmarks and their holistic configuration, we propose learning image-aware textual embedding. (c) A novel algorithm for landmarks-guided 3D head pose estimation is also proposed. (d) We refine the landmarks using head pose by innovating a 3D rendering based augmentation and pose-based batching technique while the refined landmarks, consequently improving the head pose. (e) We  report a new state-of-the-art in unsupervised facial landmark estimation across five challenging datasets including AFLW2000, MAFL, Cat-Heads, LS3D and a facial landmark tracking benchmark 300VW. In unsupervised head pose estimation, we outperform existing methods on BIWI and AFLW2000 by visible margins. Moreover, our method provides a significant training speed-up over the existing best unsupervised landmark detection method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tourani_Unsupervised_Discovery_of_Facial_Landmarks_and_Head_Pose_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tourani_Unsupervised_Discovery_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tourani_2025_CVPR,
    author    = {Tourani, Satyajit and Tourani, Siddharth and Mahmood, Arif and Khan, Muhammad Haris},
    title     = {Unsupervised Discovery of Facial Landmarks and Head Pose},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21192-21202}
}

raw_affiliation:
[{"affiliation": "IIIT Hyderabad", "city": "Hyderabad", "country": "India"}, {"affiliation": "MBZUAI", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "University of Heidelberg", "city": "Heidelberg", "country": "Germany"}, {"affiliation": "Information Technology University of the Punjab", "city": "Lahore", "country": "Pakistan"}]

affiliations:
["IIIT Hyderabad", "MBZUAI", "University of Heidelberg", "Information Technology University of the Punjab"]


authors: Sherry X. Chen, Misha Sra, Pradeep Sen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28513-28522
affiliations: ["University of California, Santa Barbara"]
cities: ["Santa Barbara"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Instruct-CLIP_Improving_Instruction-Guided_Image_Editing_with_Automated_Data_Refinement_Using_CVPR_2025_paper.html

title:
Instruct-CLIP: Improving Instruction-Guided Image Editing with Automated Data Refinement Using Contrastive Learning

abstract:
Although natural language instructions offer an intuitive way to guide automated image editing, deep-learning models often struggle to achieve high-quality results, largely due to the difficulty of creating large, high-quality training datasets. To do this, previous approaches have typically relied on text-to-image (T2I) generative models to produce pairs of original and edited images that simulate the input/output of an instruction-guided image- editing model. However, these image pairs often fail to align with the specified edit instructions due to the limitations of T2I models, which negatively impacts models trained on such datasets. To address this, we present Instruct-CLIP (I-CLIP), a selfsupervised method that learns the semantic changes between original and edited images to refine and better align the instructions in existing datasets. Furthermore, we adapt Instruct-CLIP to handle noisy latent images and diffusion timesteps so that it can be used to train latent diffusion models (LDMs) and efficiently enforce alignment between the edit instruction and the image changes in latent space at any step of the diffusion pipeline. We use Instruct-CLIP to correct the InstructPix2Pix dataset and get over 120K refined samples we then use to fine-tune their model, guided by our novel I- CLIP-based loss function. The resulting model can produce edits that are more aligned with the given instructions. Our code and dataset are available at https://github.com/SherryXTChen/Instruct-CLIP.git.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Instruct-CLIP_Improving_Instruction-Guided_Image_Editing_with_Automated_Data_Refinement_Using_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Instruct-CLIP_Improving_Instruction-Guided_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Sherry X. and Sra, Misha and Sen, Pradeep},
    title     = {Instruct-CLIP: Improving Instruction-Guided Image Editing with Automated Data Refinement Using Contrastive Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28513-28522}
}

raw_affiliation:
[{"affiliation": "University of California, Santa Barbara", "city": "Santa Barbara", "country": "USA"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26440-26450
cities: ["Shanghai", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Stabilizing_and_Accelerating_Autofocus_with_Expert_Trajectory_Regularized_Deep_Reinforcement_CVPR_2025_paper.html

title:
Stabilizing and Accelerating Autofocus with Expert Trajectory Regularized Deep Reinforcement Learning

authors:
Shouhang Zhu, Chenglin Li, Yuankun Jiang, Li Wei, Nuowen Kan, Ziyang Zheng, Wenrui Dai, Junni Zou, Hongkai Xiong

abstract:
Autofocus is a crucial component of modern digital cameras. While recent learning-based methods achieve state-of-the-art in focus prediction accuracy, they unfortunately ignore the potential focus hunting phenomenon of back-and-forth lens movement in the multi-step focusing procedure. To address this, in this paper, we propose an expert regularized deep reinforcement learning (DRL)-based approach for autofocus, which can utilize the sequential information of lens movement trajectory to both enhance the multi-step in-focus prediction accuracy and reduce the chance of focus hunting. Our method generally follows an actor-critic framework. To accelerate the DRL's training with a higher sample efficiency, we initialize the policy with a pre-trained single-step prediction network, where the network is further improved by modifying the output of absolute in-focus position distribution to the relative lens movement distribution to establish a better mapping between input images and lens movement. To further stabilize DRL's training with a lower occurrence of focus hunting in the resulting lens movement trajectory, we generate some offline trajectories based on prior knowledge to avoid focus hunting, which are then leveraged as an offline dataset of expert trajectories to regularize the actor network's training. Empirical evaluations show that our method outperforms those learning-based methods on public benchmarks, with higher single- and multi-step prediction accuracy, and a significant reduction of focus hunting rate.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Stabilizing_and_Accelerating_Autofocus_with_Expert_Trajectory_Regularized_Deep_Reinforcement_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Stabilizing_and_Accelerating_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Shouhang and Li, Chenglin and Jiang, Yuankun and Wei, Li and Kan, Nuowen and Zheng, Ziyang and Dai, Wenrui and Zou, Junni and Xiong, Hongkai},
    title     = {Stabilizing and Accelerating Autofocus with Expert Trajectory Regularized Deep Reinforcement Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26440-26450}
}

raw_affiliation:
[{"affiliation": "Department of Electronic Engineering, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Department of Computer Science and Engineering, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["Department of Electronic Engineering, Shanghai Jiao Tong University", "Department of Computer Science and Engineering, Shanghai Jiao Tong University"]


title: SharpDepth: Sharpening Metric Depth Predictions Using Diffusion Distillation
authors: Duc-Hai Pham, Tung Do, Phong Nguyen, Binh-Son Hua, Khoi Nguyen, Rang Nguyen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17060-17069
affiliations: ["Movian AI", "Trinity College Dublin", "VinAI"]
cities: ["Unknown", "Dublin", "Unknown"]
countries: ["Vietnam", "Ireland", "Vietnam"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pham_SharpDepth_Sharpening_Metric_Depth_Predictions_Using_Diffusion_Distillation_CVPR_2025_paper.html

abstract:
We propose SharpDepth, a novel approach to monocular metric depth estimation that combines the metric accuracy of discriminative depth estimation methods (e.g., Metric3D, UniDepth) with the fine-grained boundary sharpness typically achieved by generative methods (e.g., Marigold, Lotus). Traditional discriminative models trained on real-world data with sparse ground-truth depth can accurately predict metric depth but often produce over-smoothed or low-detail depth maps. Generative models, in contrast, are trained on synthetic data with dense ground truth, generating depth maps with sharp boundaries yet only providing relative depth with low accuracy. Our approach bridges these limitations by integrating metric accuracy with detailed boundary preservation, resulting in depth predictions that are both metrically precise and visually sharp. Our extensive zero-shot evaluations on standard depth estimation benchmarks confirm SharpDepth's effectiveness, showing its ability to achieve both high depth accuracy and detailed representation, making it well-suited for applications requiring high-quality depth perception across diverse, real-world environments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pham_SharpDepth_Sharpening_Metric_Depth_Predictions_Using_Diffusion_Distillation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pham_SharpDepth_Sharpening_Metric_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pham_2025_CVPR,
    author    = {Pham, Duc-Hai and Do, Tung and Nguyen, Phong and Hua, Binh-Son and Nguyen, Khoi and Nguyen, Rang},
    title     = {SharpDepth: Sharpening Metric Depth Predictions Using Diffusion Distillation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17060-17069}
}

raw_affiliation:
[{"affiliation": "Movian AI", "city": "Unknown", "country": "Vietnam"}, {"affiliation": "Trinity College Dublin", "city": "Dublin", "country": "Ireland"}, {"affiliation": "VinAI", "city": "Unknown", "country": "Vietnam"}]


title: Repurposing Stable Diffusion Attention for Training-Free Unsupervised Interactive Segmentation
authors: Markus Karmann, Onay Urfalioglu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24518-24528
raw_affiliation: [{"affiliation": "Vivo Tech Research GmbH", "city": "Munich", "country": "Germany"}]
affiliations: ["Vivo Tech Research GmbH"]
cities: ["Munich"]
countries: ["Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Karmann_Repurposing_Stable_Diffusion_Attention_for_Training-Free_Unsupervised_Interactive_Segmentation_CVPR_2025_paper.html

abstract:
Recent progress in interactive point prompt based Image Segmentation allows to significantly reduce the manual effort to obtain high quality semantic labels. State-of-the-art unsupervised methods use self-supervised pre-trained models to obtain pseudo-labels which are used in training a prompt-based segmentation model. In this paper, we propose a novel unsupervised and training-free approach based solely on the self-attention of Stable Diffusion. We interpret the self-attention tensor as a Markov transition operator, which enables us to iteratively construct a Markov chain. Pixel-wise counting of the required number of iterations along the Markov chain to reach a relative probability threshold yields a Markov-iteration-map, which we simply call a Markov-map. Compared to the raw attention maps, we show that our proposed Markov-map has less noise, sharper semantic boundaries and more uniform values within semantically similar regions. We integrate the Markov-map in a simple yet effective truncated nearest neighbor framework to obtain interactive point prompt based segmentation. Despite being training-free, we experimentally show that our approach yields excellent results in terms of Number of Clicks (NoC), even outperforming state-of-the-art training based unsupervised methods in most of the datasets. Code is available at https://github.com/mkarmann/m2n2.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Karmann_Repurposing_Stable_Diffusion_Attention_for_Training-Free_Unsupervised_Interactive_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Karmann_Repurposing_Stable_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Karmann_2025_CVPR,
    author    = {Karmann, Markus and Urfalioglu, Onay},
    title     = {Repurposing Stable Diffusion Attention for Training-Free Unsupervised Interactive Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24518-24528}
}


title: GO-N3RDet: Geometry Optimized NeRF-enhanced 3D Object Detector
authors: Zechuan Li, Hongshan Yu, Yihao Ding, Jinhao Qiao, Basim Azam, Naveed Akhtar
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27211-27221
affiliations: ["Hunan University", "The University of Melbourne"]
cities: ["Changsha", "Melbourne"]
countries: ["China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_GO-N3RDet_Geometry_Optimized_NeRF-enhanced_3D_Object_Detector_CVPR_2025_paper.html

abstract:
We propose GO-N3RDet, a scene-geometry optimized multi-view 3D object detector enhanced by neural radiance fields. The key to accurate 3D object detection is in effective voxel representation. However, due to occlusion and lack of 3D information, constructing 3D features from multi-view 2D images is challenging. Addressing that, we introduce a unique 3D positional information embedded voxel optimization mechanism to fuse multi-view features. To prioritize neural field reconstruction in object regions, we also devise a double importance sampling scheme for the NeRF branch of our detector. We additionally propose an opacity optimization module for precise voxel opacity prediction by enforcing multi-view consistency constraints. Moreover, to further improve voxel density consistency across multiple perspectives, we incorporate ray distance as a weighting factor to minimize cumulative ray errors. Our unique modules synergetically form an end-to-end neural model that establishes new state-of-the-art in NeRF-based multi-view 3D detection, verified with extensive experiments on ScanNet and ARKITScenes. Code will be available at https://github.com/ZechuanLi/GO-N3RDet.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_GO-N3RDet_Geometry_Optimized_NeRF-enhanced_3D_Object_Detector_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_GO-N3RDet_Geometry_Optimized_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Zechuan and Yu, Hongshan and Ding, Yihao and Qiao, Jinhao and Azam, Basim and Akhtar, Naveed},
    title     = {GO-N3RDet: Geometry Optimized NeRF-enhanced 3D Object Detector},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27211-27221}
}

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


title: DPSeg: Dual-Prompt Cost Volume Learning for Open-Vocabulary Semantic Segmentation
authors: Ziyu Zhao, Xiaoguang Li, Lingjia Shi, Nasrin Imanpour, Song Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25346-25356
affiliations: ["University of South Carolina, USA", "Shenzhen University of Advanced Technology, China"]
cities: ["Columbia", "Shenzhen"]
countries: ["USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_DPSeg_Dual-Prompt_Cost_Volume_Learning_for_Open-Vocabulary_Semantic_Segmentation_CVPR_2025_paper.html

abstract:
Open-vocabulary semantic segmentation aims to segment images into distinct semantic regions for both seen and unseen categories at the pixel level. Current methods utilize text embeddings from pre-trained vision-language models like CLIP but struggle with the inherent domain gap between image and text embeddings, even after extensive alignment during training. Additionally, relying solely on deep text-aligned features limits shallow-level feature guidance, which is crucial for detecting small objects and fine details, ultimately reducing segmentation accuracy. To address these limitations, we propose a dual prompting framework, DPSeg, for this task. Our approach combines dual-prompt cost volume generation, a cost volume-guided decoder, and a semantic-guided prompt refinement strategy that leverages our dual prompting scheme to mitigate alignment issues in visual prompt generation. By incorporating visual embeddings from a visual prompt encoder, our approach reduces the domain gap between text and image embeddings while providing multi-level guidance through shallow features. Extensive experiments demonstrate that our method significantly outperforms existing state-of-the-art approaches on multiple public datasets.

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

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Ziyu and Li, Xiaoguang and Shi, Lingjia and Imanpour, Nasrin and Wang, Song},
    title     = {DPSeg: Dual-Prompt Cost Volume Learning for Open-Vocabulary Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25346-25356}
}

raw_affiliation:
[{"affiliation": "University of South Carolina, USA", "city": "Columbia", "country": "USA"}, {"affiliation": "Shenzhen University of Advanced Technology, China", "city": "Shenzhen", "country": "China"}]


authors: Shuoyan Wei, Feng Li, Shengeng Tang, Yao Zhao, Huihui Bai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17755-17766
cities: ["Beijing", "Beijing", "Hefei", "Tangshan"]
countries: ["China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wei_EvEnhancer_Empowering_Effectiveness_Efficiency_and_Generalizability_for_Continuous_Space-Time_Video_CVPR_2025_paper.html

title:
EvEnhancer: Empowering Effectiveness, Efficiency and Generalizability for Continuous Space-Time Video Super-Resolution with Events

abstract:
Continuous space-time video super-resolution (C-STVSR) endeavors to upscale videos simultaneously at arbitrary spatial and temporal scales, which has recently garnered increasing interest. However, prevailing methods struggle to yield satisfactory videos at out-of-distribution spatial and temporal scales. On the other hand, event streams characterized by high temporal resolution and high dynamic range, exhibit compelling promise in vision tasks. This paper presents EvEnhancer, an innovative approach that marries the unique advantages of event streams to elevate effectiveness, efficiency, and generalizability for C-STVSR. Our approach hinges on two pivotal components: 1) Event-adapted synthesis capitalizes on the spatiotemporal correlations between frames and events to discern and learn long-term motion trajectories, enabling the adaptive interpolation and fusion of informative spatiotemporal features; 2) Local implicit video transformer integrates local implicit video neural function with cross-scale spatiotemporal attention to learn continuous video representations utilized to generate plausible videos at arbitrary resolutions and frame rates. Experiments show that EvEnhancer achieves superiority on synthetic and real-world datasets and preferable generalizability on out-of-distribution scales against state-of-the-art methods. Code is available at https://github.com/W-Shuoyan/EvEnhancer.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wei_EvEnhancer_Empowering_Effectiveness_Efficiency_and_Generalizability_for_Continuous_Space-Time_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wei_EvEnhancer_Empowering_Effectiveness_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wei_2025_CVPR,
    author    = {Wei, Shuoyan and Li, Feng and Tang, Shengeng and Zhao, Yao and Bai, Huihui},
    title     = {EvEnhancer: Empowering Effectiveness, Efficiency and Generalizability for Continuous Space-Time Video Super-Resolution with Events},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17755-17766}
}

raw_affiliation:
[{"affiliation": "Institute of Information Science, Beijing Jiaotong University", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Key Laboratory of Advanced Information Science and Network Technology", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science and Engineering, Hefei University of Technology", "city": "Hefei", "country": "China"}, {"affiliation": "Tangshan Research Institute of Beijing Jiaotong University", "city": "Tangshan", "country": "China"}]

affiliations:
["Institute of Information Science, Beijing Jiaotong University", "Beijing Key Laboratory of Advanced Information Science and Network Technology", "School of Computer Science and Engineering, Hefei University of Technology", "Tangshan Research Institute of Beijing Jiaotong University"]


title: Seeing A 3D World in A Grain of Sand
authors: Yufan Zhang, Yu Ji, Yu Guo, Jinwei Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11187-11196
affiliations: ["George Mason University", "LightThought LLC"]
cities: ["Fairfax", "Unknown"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Seeing_A_3D_World_in_A_Grain_of_Sand_CVPR_2025_paper.html

abstract:
We present a snapshot imaging technique for recovering 3D surrounding views of miniature scenes. Due to their intricacy, miniature scenes with objects sized in millimeters are difficult to reconstruct, yet miniatures are common in life and their 3D digitalization is desirable. We design a catadioptric imaging system with a single camera and eight pairs of planar mirrors for snapshot 3D reconstruction from a dollhouse perspective. We place paired mirrors on nested pyramid surfaces for capturing surrounding multi-view images in a single shot. Our mirror design is customizable based on the size of the scene for optimized view coverage. We use the 3D Gaussian Splatting (3DGS) representation for scene reconstruction and novel view synthesis. We overcome the challenge posed by our sparse view input by integrating visual hull-derived depth constraint. Our method demonstrates state-of-the-art performance on a variety of synthetic and real miniature scenes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Seeing_A_3D_World_in_A_Grain_of_Sand_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Seeing_A_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Yufan and Ji, Yu and Guo, Yu and Ye, Jinwei},
    title     = {Seeing A 3D World in A Grain of Sand},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11187-11196}
}

raw_affiliation:
[{"affiliation": "George Mason University", "city": "Fairfax", "country": "USA"}, {"affiliation": "LightThought LLC", "city": "Unknown", "country": "USA"}]


authors: Xinyue Zhang, Zijia Dai, Wanting Xu, Laurent Kneip
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27103-27112
affiliations: ["Mobile Perception Lab, ShanghaiTech University"]
cities: ["Shanghai"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Simulator_HC_Regression-based_Online_Simulation_of_Starting_Problem-Solution_Pairs_for_CVPR_2025_paper.html

title:
Simulator HC: Regression-based Online Simulation of Starting Problem-Solution Pairs for Homotopy Continuation in Geometric Vision

abstract:
While automatically generated polynomial elimination templates have sparked great progress in the field of 3D computer vision, there remain many problems for which the degree of the constraints or the number of unknowns leads to intractability. In recent years, homotopy continuation has been introduced as a plausible alternative. However, the method currently depends on expensive parallel tracking of all possible solutions in the complex domain, or a classification network for starting problem-solution pairs trained over a limited set of real-world examples. Our innovation lies in a novel approach to finding solution-problem pairs, where we only need to predict a rough initial solution, with the corresponding problem generated by an online simulator. Subsequently, homotopy continuation is applied to track that single solution back to the original problem. We apply this elegant combination to generalized camera resectioning, and also introduce a new solution to the challenging generalized relative pose and scale problem. As demonstrated, the proposed method successfully compensates the raw error committed by the regressor alone, and leads to state-of-the-art efficiency and success rates.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Simulator_HC_Regression-based_Online_Simulation_of_Starting_Problem-Solution_Pairs_for_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Xinyue and Dai, Zijia and Xu, Wanting and Kneip, Laurent},
    title     = {Simulator HC: Regression-based Online Simulation of Starting Problem-Solution Pairs for Homotopy Continuation in Geometric Vision},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27103-27112}
}

raw_affiliation:
[{"affiliation": "Mobile Perception Lab, ShanghaiTech University", "city": "Shanghai", "country": "China"}]


title: Dynamic Integration of Task-Specific Adapters for Class Incremental Learning
authors: Jiashuo Li, Shaokun Wang, Bo Qian, Yuhang He, Xing Wei, Qiang Wang, Yihong Gong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30545-30555
cities: ["Xi'an", "Xi'an"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Dynamic_Integration_of_Task-Specific_Adapters_for_Class_Incremental_Learning_CVPR_2025_paper.html

abstract:
Non-exemplar Class Incremental Learning (NECIL) enables models to continuously acquire new classes without retraining from scratch and storing old task exemplars, addressing privacy and storage issues. However, the absence of data from earlier tasks exacerbates the challenge of catastrophic forgetting in NECIL. In this paper, we propose a novel framework called Dynamic Integration of task-specific Adapters (DIA), which comprises two key components: Task-Specific Adapter Integration (TSAI) and Patch-Level Model Alignment. TSAI boosts compositionality through a patch-level adapter integration strategy, aggregating richer task-specific information while maintaining low computation costs. Patch-Level Model Alignment maintains feature consistency and accurate decision boundaries via two specialized mechanisms: Patch-Level Distillation Loss (PDL) and Patch-Level Feature Reconstruction (PFR). Specifically, on the one hand, the PDL preserves feature-level consistency between successive models by implementing a distillation loss based on the contributions of patch tokens to new class learning. On the other hand, the PFR promotes classifier alignment by reconstructing old class features from previous tasks that adapt to new task knowledge, thereby preserving well-calibrated decision boundaries. Comprehensive experiments validate the effectiveness of our DIA, revealing significant improvements on NECIL benchmark datasets while maintaining an optimal balance between computational complexity and accuracy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Dynamic_Integration_of_Task-Specific_Adapters_for_Class_Incremental_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Dynamic_Integration_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Jiashuo and Wang, Shaokun and Qian, Bo and He, Yuhang and Wei, Xing and Wang, Qiang and Gong, Yihong},
    title     = {Dynamic Integration of Task-Specific Adapters for Class Incremental Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30545-30555}
}

raw_affiliation:
[{"affiliation": "School of Software Engineering, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "College of Artificial Intelligence, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}]

affiliations:
["School of Software Engineering, Xi\u2019an Jiaotong University", "College of Artificial Intelligence, Xi\u2019an Jiaotong University"]


authors: Yuxiang Fu, Qi Yan, Lele Wang, Ke Li, Renjie Liao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17282-17293
cities: ["Vancouver", "Toronto", "Toronto", "Burnaby"]
countries: ["Canada", "Canada", "Canada", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fu_MoFlow_One-Step_Flow_Matching_for_Human_Trajectory_Forecasting_via_Implicit_CVPR_2025_paper.html

title:
MoFlow: One-Step Flow Matching for Human Trajectory Forecasting via Implicit Maximum Likelihood Estimation based Distillation

abstract:
In this paper, we address the problem of human trajectory forecasting, which aims to predict the inherently multi-modal future movements of humans based on their past trajectories and other contextual cues. We propose a novel motion prediction conditional flow matching model, termed MoFlow, to predict K-shot future trajectories for all agents in a given scene. We design a novel flow matching loss function that not only ensures at least one of the K sets of future trajectories is accurate but also encourages all K sets of future trajectories to be diverse and plausible. Furthermore, by leveraging the implicit maximum likelihood estimation (IMLE), we propose a novel distillation method for flow models that only requires samples from the teacher model.  Extensive experiments on the real-world datasets, including SportVU NBA games, ETH-UCY, and SDD, demonstrate that both our teacher flow model and the IMLE-distilled student model achieve state-of-the-art performance. These models can generate diverse trajectories that are physically and socially plausible. Moreover, our one-step student model is 100 times faster than the teacher flow model during sampling. The code, model, and data are available at our project page: https://moflow-imle.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fu_MoFlow_One-Step_Flow_Matching_for_Human_Trajectory_Forecasting_via_Implicit_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fu_MoFlow_One-Step_Flow_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Fu_2025_CVPR,
    author    = {Fu, Yuxiang and Yan, Qi and Wang, Lele and Li, Ke and Liao, Renjie},
    title     = {MoFlow: One-Step Flow Matching for Human Trajectory Forecasting via Implicit Maximum Likelihood Estimation based Distillation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17282-17293}
}

raw_affiliation:
[{"affiliation": "University of British Columbia", "city": "Vancouver", "country": "Canada"}, {"affiliation": "Vector Institute for AI", "city": "Toronto", "country": "Canada"}, {"affiliation": "Canada CIFAR AI Chair", "city": "Toronto", "country": "Canada"}, {"affiliation": "Simon Fraser University", "city": "Burnaby", "country": "Canada"}]

affiliations:
["University of British Columbia", "Vector Institute for AI", "Canada CIFAR AI Chair", "Simon Fraser University"]


title: EgoPressure: A Dataset for Hand Pressure and Pose Estimation in Egocentric Vision
authors: Yiming Zhao, Taein Kwon, Paul Streli, Marc Pollefeys, Christian Holz
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27727-27738
affiliations: ["ETH Z\u00fcrich", "Microsoft Spatial AI Lab, Z\u00fcrich"]
cities: ["Z\u00fcrich", "Z\u00fcrich"]
countries: ["Switzerland", "Switzerland"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_EgoPressure_A_Dataset_for_Hand_Pressure_and_Pose_Estimation_in_CVPR_2025_paper.html

abstract:
Touch contact and pressure are essential for understanding how humans interact with objects and offer insights that benefit applications in mixed reality and robotics. Estimating these interactions from an egocentric camera perspective is challenging, largely due to the lack of comprehensive datasets that provide both hand poses and pressure annotations. In this paper, we present EgoPressure, an egocentric dataset that is annotated with high-resolution pressure intensities at contact points and precise hand pose meshes, obtained via our multi-view, sequence-based optimization method. We introduce baseline models for estimating applied pressure on external surfaces from RGB images, both with and without hand pose information, as well as a joint model for predicting hand pose and the pressure distribution across the hand mesh.Our experiments show that pressure and hand pose complement each other in understanding hand-object interactions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_EgoPressure_A_Dataset_for_Hand_Pressure_and_Pose_Estimation_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_EgoPressure_A_Dataset_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Yiming and Kwon, Taein and Streli, Paul and Pollefeys, Marc and Holz, Christian},
    title     = {EgoPressure: A Dataset for Hand Pressure and Pose Estimation in Egocentric Vision},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27727-27738}
}

raw_affiliation:
[{"affiliation": "ETH Z\u00fcrich", "city": "Z\u00fcrich", "country": "Switzerland"}, {"affiliation": "Microsoft Spatial AI Lab, Z\u00fcrich", "city": "Z\u00fcrich", "country": "Switzerland"}]


title: DiverseFlow: Sample-Efficient Diverse Mode Coverage in Flows
authors: Mashrur M. Morshed, Vishnu Boddeti
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23303-23312
raw_affiliation: [{"affiliation": "Michigan State University", "city": "East Lansing", "country": "USA"}]
affiliations: ["Michigan State University"]
cities: ["East Lansing"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Morshed_DiverseFlow_Sample-Efficient_Diverse_Mode_Coverage_in_Flows_CVPR_2025_paper.html

abstract:
Many real-world applications of flow-based generative models desire a diverse set of samples that cover multiple modes of the target distribution. However, the predominant approach for obtaining diverse sets is not sample-efficient, as it involves independently obtaining many samples from the source distribution and mapping them through the flow until the desired mode coverage is achieved. As an alternative to repeated sampling, we introduce DiverseFlow: a training-free approach to improve the diversity of flow models. Our key idea is to employ a determinantal point process to induce a coupling between the samples that drives diversity under a fixed sampling budget. In essence, DiverseFlow allows exploration of more variations in a learned flow model with fewer samples. We demonstrate the efficacy of our method for tasks where sample-efficient diversity is desirable, such as text-guided image generation with polysemous words, inverse problems like large-hole inpainting, and class-conditional image synthesis.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Morshed_DiverseFlow_Sample-Efficient_Diverse_Mode_Coverage_in_Flows_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Morshed_DiverseFlow_Sample-Efficient_Diverse_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Morshed_2025_CVPR,
    author    = {Morshed, Mashrur M. and Boddeti, Vishnu},
    title     = {DiverseFlow: Sample-Efficient Diverse Mode Coverage in Flows},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23303-23312}
}


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14400-14410
cities: ["Beijing", "Beijing", "Redmond", "Beijing", "Adelaide"]
countries: ["China", "China", "USA", "China", "Australia"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_Reason-before-Retrieve_One-Stage_Reflective_Chain-of-Thoughts_for_Training-Free_Zero-Shot_Composed_Image_Retrieval_CVPR_2025_paper.html

title:
Reason-before-Retrieve: One-Stage Reflective Chain-of-Thoughts for Training-Free Zero-Shot Composed Image Retrieval

authors:
Yuanmin Tang, Jue Zhang, Xiaoting Qin, Jing Yu, Gaopeng Gou, Gang Xiong, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Wu

abstract:
Composed Image Retrieval (CIR) aims to retrieve target images that closely resemble a reference image while integrating user-specified textual modifications, thereby capturing user intent more accurately. Existing training-free zero-shot CIR (ZS-CIR) methods often employ a two-stage process: they first generate a caption for the reference image and then use Large Language Models for reasoning a target description. However, these methods suffer from missing critical visual details and limited reasoning capabilities, leading to suboptimal retrieval performance. To address these challenges, we propose a novel, training-free one-stage method, One-Stage Reflective Chain-of-Thought Reasoning (OSrCIR) for ZS-CIR, which employs Multimodal Large Language Models to retain essential visual information in a single-stage reasoning process, eliminating the information loss in two-stage methods. Our Reflective Chain-of-Thought framework further improves interpretative accuracy by aligning manipulation intent with contextual cues from reference images. OSrCIR achieves performance gains of 1.80% to 6.44% over existing training-free methods across multiple tasks, setting new state-of-the-art results in ZS-CIR and enhancing its utility in vision-language applications. Our code is available at https://github.com/microsoft/ACV/tree/main/OSrCIR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_Reason-before-Retrieve_One-Stage_Reflective_Chain-of-Thoughts_for_Training-Free_Zero-Shot_Composed_Image_Retrieval_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_Reason-before-Retrieve_One-Stage_Reflective_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Yuanmin and Zhang, Jue and Qin, Xiaoting and Yu, Jing and Gou, Gaopeng and Xiong, Gang and Lin, Qingwei and Rajmohan, Saravan and Zhang, Dongmei and Wu, Qi},
    title     = {Reason-before-Retrieve: One-Stage Reflective Chain-of-Thoughts for Training-Free Zero-Shot Composed Image Retrieval},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14400-14410}
}

raw_affiliation:
[{"affiliation": "Institute of Information Engineering, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}, {"affiliation": "Minzu University of China", "city": "Beijing", "country": "China"}, {"affiliation": "University of Adelaide", "city": "Adelaide", "country": "Australia"}]

affiliations:
["Institute of Information Engineering, Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "Microsoft", "Minzu University of China", "University of Adelaide"]


title: UniGraspTransformer: Simplified Policy Distillation for Scalable Dexterous Robotic Grasping
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12199-12208
affiliations: ["Microsoft Research Asia", "University of Sydney", "National University of Singapore"]
cities: ["Beijing", "Sydney", "Singapore"]
countries: ["China", "Australia", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_UniGraspTransformer_Simplified_Policy_Distillation_for_Scalable_Dexterous_Robotic_Grasping_CVPR_2025_paper.html

authors:
Wenbo Wang, Fangyun Wei, Lei Zhou, Xi Chen, Lin Luo, Xiaohan Yi, Yizhong Zhang, Yaobo Liang, Chang Xu, Yan Lu, Jiaolong Yang, Baining Guo

abstract:
We introduce UniGraspTransformer, a universal Transformer-based network for dexterous robotic grasping that simplifies training while enhancing scalability and performance. Unlike prior methods such as UniDexGrasp++, which require complex, multi-step training pipelines, UniGraspTransformer follows a streamlined process: first, dedicated policy networks are trained for individual objects using reinforcement learning to generate successful grasp trajectories; then, these trajectories are distilled into a single, universal network. Our approach enables UniGraspTransformer to scale effectively, incorporating up to 12 self-attention blocks for handling thousands of objects with diverse poses. Additionally, it generalizes well to both idealized and real-world inputs, evaluated in state-based and vision-based settings. Notably, UniGraspTransformer generates a broader range of grasping poses for objects in various shapes and orientations, resulting in more diverse grasp strategies. Experimental results demonstrate significant improvements over state-of-the-art, UniDexGrasp++, across various object categories, achieving success rate gains of 3.5%, 7.7%, and 10.1% on seen objects, unseen objects within seen categories, and completely unseen objects, respectively, in the vision-based setting. Project page: https://dexhand.github.io/UniGraspTransformer/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_UniGraspTransformer_Simplified_Policy_Distillation_for_Scalable_Dexterous_Robotic_Grasping_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_UniGraspTransformer_Simplified_Policy_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Wenbo and Wei, Fangyun and Zhou, Lei and Chen, Xi and Luo, Lin and Yi, Xiaohan and Zhang, Yizhong and Liang, Yaobo and Xu, Chang and Lu, Yan and Yang, Jiaolong and Guo, Baining},
    title     = {UniGraspTransformer: Simplified Policy Distillation for Scalable Dexterous Robotic Grasping},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12199-12208}
}

raw_affiliation:
[{"affiliation": "Microsoft Research Asia", "city": "Beijing", "country": "China"}, {"affiliation": "University of Sydney", "city": "Sydney", "country": "Australia"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]


title: GeoMM: On Geodesic Perspective for Multi-modal Learning
authors: Shibin Mei, Hang Wang, Bingbing Ni
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4776-4786
affiliations: ["Huawei", "Shanghai Jiao Tong University"]
cities: ["Shenzhen", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mei_GeoMM_On_Geodesic_Perspective_for_Multi-modal_Learning_CVPR_2025_paper.html

abstract:
Geodesic distance serves as a reliable means of measuring distance in nonlinear spaces, and such nonlinear manifolds are prevalent in the current multimodal learning. In these scenarios, some samples may exhibit high similarity, yet they convey different semantics, making traditional distance metrics inadequate for distinguishing between positive and negative samples. This paper introduces geodesic distance as a novel distance metric in multi-modal learning for the first time, to mine correlations between samples, aiming to address the limitations of common distance metric. Our approach incorporates a comprehensive series of strategies to adapt geodesic distance for the current multimodal learning. Specifically, we construct a graph structure to represent the adjacency relationships among samples by thresholding distances between them and then apply the shortest-path algorithm to obtain geodesic distance within this graph. To facilitate efficient computation, we further propose a hierarchical graph structure through clustering and combined with incremental update strategies for dynamic status updates. Extensive experiments across various downstream tasks validate the effectiveness of our proposed method, demonstrating its capability to capture complex relationships between samples and improve the performance of multimodal learning models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mei_GeoMM_On_Geodesic_Perspective_for_Multi-modal_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mei_GeoMM_On_Geodesic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mei_2025_CVPR,
    author    = {Mei, Shibin and Wang, Hang and Ni, Bingbing},
    title     = {GeoMM: On Geodesic Perspective for Multi-modal Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4776-4786}
}

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


authors: Xueqing Wu, Yuheng Ding, Bingxuan Li, Pan Lu, Da Yin, Kai-Wei Chang, Nanyun Peng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9527-9537
affiliations: ["University of California, Los-Angeles", "Stanford"]
cities: ["Los Angeles", "Stanford"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_VISCO_Benchmarking_Fine-Grained_Critique_and_Correction_Towards_Self-Improvement_in_Visual_CVPR_2025_paper.html

title:
VISCO: Benchmarking Fine-Grained Critique and Correction Towards Self-Improvement in Visual Reasoning

abstract:
The ability of large vision-language models (LVLMs) to critique and correct their reasoning is an essential building block towards their self-improvement. However, a systematic analysis of such capabilities in LVLMs is still lacking. We propose VISCO, the first benchmark to extensively analyze the fine-grained critique and correction capabilities of LVLMs. Compared to existing work that uses a single scalar value to critique the entire reasoning [4], VISCO features **dense** and **fine-grained** critique, requiring LVLMs to evaluate the correctness of each step in the chain-of-thought and provide natural language explanations to support their judgments. Extensive evaluation of 24 LVLMs demonstrates that human-written critiques significantly enhance the performance after correction, showcasing the potential of the self-improvement strategy. However, the model-generated critiques are less helpful and sometimes detrimental to the performance, suggesting that critique is the crucial bottleneck. We identified three common patterns in critique failures: failure to critique visual perception, reluctance to "say no", and exaggerated assumption of error propagation. To address these issues, we propose an effective LookBack strategy that revisits the image to verify each piece of information in the initial reasoning. \ourscritic   significantly improves critique and correction performance by up to 13.5%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_VISCO_Benchmarking_Fine-Grained_Critique_and_Correction_Towards_Self-Improvement_in_Visual_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_VISCO_Benchmarking_Fine-Grained_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Xueqing and Ding, Yuheng and Li, Bingxuan and Lu, Pan and Yin, Da and Chang, Kai-Wei and Peng, Nanyun},
    title     = {VISCO: Benchmarking Fine-Grained Critique and Correction Towards Self-Improvement in Visual Reasoning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9527-9537}
}

raw_affiliation:
[{"affiliation": "University of California, Los-Angeles", "city": "Los Angeles", "country": "USA"}, {"affiliation": "Stanford", "city": "Stanford", "country": "USA"}]


title: MaskGWM: A Generalizable Driving World Model with Video Mask Reconstruction
authors: Jingcheng Ni, Yuxin Guo, Yichen Liu, Rui Chen, Lewei Lu, Zehuan Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22381-22391
raw_affiliation: [{"affiliation": "SenseTime Research", "city": "Hong Kong", "country": "China"}]
affiliations: ["SenseTime Research"]
cities: ["Hong Kong"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ni_MaskGWM_A_Generalizable_Driving_World_Model_with_Video_Mask_Reconstruction_CVPR_2025_paper.html

abstract:
World models that forecast environmental changes from actions are vital for autonomous driving models with strong generalization. The prevailing driving world model mainly build on pixel-level video prediction model. Although these models can produce high-fidelity video sequences with advanced diffusion-based generator, they are constrained by their predictive duration and overall generalization capabilities. In this paper, we explore to solve this problem by combining pixel-level generation loss with MAE-style feature-level context learning. In particular, we instantiate this target with three key design: (1) A more scalable Diffusion Transformer (DiT) structure trained with extra mask construction task. (2) we devise diffusion-related mask tokens to deal with the fuzzy relations between mask reconstruction and generative diffusion process. (3) we extend mask construction task to spatial-temporal domain by utilizing row-wise mask for shifted self-attention rather than masked self-attention in MAE. Then, we adopt a row-wise cross-view module to align with this mask design. Based on above improvement, we propose MaskGWM: a Generalizable driving World Model embodied with Video Mask reconstruction. Our model contains two variants: MaskGWM-long, focusing on long-horizon prediction, and MaskGWM-mview, dedicated to multi-view generation.Comprehensive experiments on standard benchmarks validate the effectiveness of the proposed method, which contain normal validation of Nuscene dataset, long-horizon rollout of OpenDV-2K dataset and zero-shot validation of Waymo dataset. Quantitative metrics on these datasets show our method notably improving state-of-the-art driving world model.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ni_MaskGWM_A_Generalizable_Driving_World_Model_with_Video_Mask_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ni_MaskGWM_A_Generalizable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ni_2025_CVPR,
    author    = {Ni, Jingcheng and Guo, Yuxin and Liu, Yichen and Chen, Rui and Lu, Lewei and Wu, Zehuan},
    title     = {MaskGWM: A Generalizable Driving World Model with Video Mask Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22381-22391}
}


title: 3D-MVP: 3D Multiview Pretraining for Manipulation
authors: Shengyi Qian, Kaichun Mo, Valts Blukis, David F. Fouhey, Dieter Fox, Ankit Goyal
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22530-22539
affiliations: ["NVIDIA", "University of Michigan", "New York University"]
cities: ["Santa Clara", "Ann Arbor", "New York"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qian_3D-MVP_3D_Multiview_Pretraining_for_Manipulation_CVPR_2025_paper.html

abstract:
Recent works have shown that visual pretraining on egocentric datasets using masked autoencoders (MAE) can improve generalization for downstream robotics tasks. However, these approaches pretrain only on 2D images, while many robotics applications require 3D scene understanding. In this work, we propose 3D-MVP, a novel approach for 3D Multi-View Pretraining using masked autoencoders. We leverage Robotic View Transformer (RVT), which uses a multi-view transformer to understand the 3D scene and predict gripper pose actions. We split RVT's multi-view transformer into visual encoder and action decoder, and pretrain its visual encoder using masked autoencoding on large-scale 3D datasets such as Objaverse. We evaluate 3D-MVP on a suite of virtual robot manipulation tasks and demonstrate improved performance over baselines. Our results suggest that 3D-aware pretraining is a promising approach to improve generalization of vision-based robotic manipulation policies.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qian_3D-MVP_3D_Multiview_Pretraining_for_Manipulation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Qian_2025_CVPR,
    author    = {Qian, Shengyi and Mo, Kaichun and Blukis, Valts and Fouhey, David F. and Fox, Dieter and Goyal, Ankit},
    title     = {3D-MVP: 3D Multiview Pretraining for Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22530-22539}
}

raw_affiliation:
[{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "New York University", "city": "New York", "country": "USA"}]


title: Enhanced OoD Detection through Cross-Modal Alignment of Multi-Modal Representations
authors: Jeonghyeon Kim, Sangheum Hwang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29979-29988
affiliations: ["Seoul National University of Science and Technology"]
cities: ["Seoul"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Enhanced_OoD_Detection_through_Cross-Modal_Alignment_of_Multi-Modal_Representations_CVPR_2025_paper.html

abstract:
Prior research on out-of-distribution detection (OoDD) has primarily focused on single-modality models. Recently, with the advent of large-scale pretrained vision-language models such as CLIP, OoDD methods utilizing such multi-modal representations through zero-shot and prompt learning strategies have emerged. However, these methods typically involve either freezing the pretrained weights or only partially tuning them, which can be suboptimal for downstream datasets. In this paper, we highlight that multi-modal fine-tuning (MMFT) can achieve notable OoDD performance. Despite some recent works demonstrating the impact of fine-tuning methods for OoDD, there remains significant potential for performance improvement. We investigate the limitation of naive fine-tuning methods, examining why they fail to fully leverage the pretrained knowledge. Our empirical analysis suggests that this issue could stem from the modality gap within in-distribution (ID) embeddings. To address this, we propose a training objective that enhances cross-modal alignment by regularizing the distances between image and text embeddings of ID data. This adjustment helps in better utilizing pretrained textual information by aligning similar semantics from different modalities (i.e., text and image) more closely in the hyperspherical representation space. We theoretically demonstrate that the proposed regularization corresponds to the maximum likelihood estimation of an energy-based model on a hypersphere. Utilizing ImageNet-1k OoD benchmark datasets, we show that our method, combined with post-hoc OoDD approaches leveraging pretrained knowledge (e.g., NegLabel), significantly outperforms existing methods, achieving state-of-the-art OoDD performance and leading ID accuracy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Enhanced_OoD_Detection_through_Cross-Modal_Alignment_of_Multi-Modal_Representations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Enhanced_OoD_Detection_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Jeonghyeon and Hwang, Sangheum},
    title     = {Enhanced OoD Detection through Cross-Modal Alignment of Multi-Modal Representations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29979-29988}
}

raw_affiliation:
[{"affiliation": "Seoul National University of Science and Technology", "city": "Seoul", "country": "South Korea"}]


title: Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution
authors: Hang Xu, Jie Huang, Wei Yu, Jiangtong Tan, Zhen Zou, Feng Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7513-7523
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Adaptive_Dropout_Unleashing_Dropout_across_Layers_for_Generalizable_Image_Super-Resolution_CVPR_2025_paper.html

abstract:
Blind Super-Resolution(blind SR) aims to enhance the model's generalization ability with unknown degradation, yet it still encounters severe overfitting issues. Some previous methods inspired by dropout, which enhances generalization by regularizing features, have shown promising results in blind SR. Nevertheless, these methods focus solely on regularizing features before the final layer and overlook the need for generalization in features at intermediate layers. Without explicit regularization of features at intermediate layers, the blind SR network struggles to obtain well-generalized feature representations. However, the key challenge is that directly applying dropout to intermediate layers leads to a significant performance drop, which we attribute to the inconsistency in training-testing and across layers it introduced. Therefore, we propose Adaptive Dropout, a new regularization method for blind SR models, which mitigates the inconsistency and facilitates application across intermediate layers of networks. Specifically, for training-testing inconsistency, we re-design the form of dropout and integrate the features before and after dropout adaptively. For inconsistency in generalization requirements across different layers, we innovatively design an adaptive training strategy to strengthen feature propagation by layer-wise annealing. Experimental results show that our method outperforms all past regularization methods on both synthetic and real-world benchmark datasets, also highly effective in other image restoration tasks.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_Adaptive_Dropout_Unleashing_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Hang and Huang, Jie and Yu, Wei and Tan, Jiangtong and Zou, Zhen and Zhao, Feng},
    title     = {Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7513-7523}
}

raw_affiliation:
[{"affiliation": "MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China", "city": "Hefei", "country": "China"}]

affiliations:
["MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China"]


title: Breaking the Memory Barrier of Contrastive Loss via Tile-Based Strategy
authors: Zesen Cheng, Hang Zhang, Kehan Li, Sicong Leng, Zhiqiang Hu, Fei Wu, Deli Zhao, Xin Li, Lidong Bing
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10036-10045
affiliations: ["Alibaba Group", "Zhejiang University", "Shanda AI Research Institute"]
cities: ["Hangzhou", "Hangzhou", "Unknown"]
countries: ["China", "China", "Unknown"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cheng_Breaking_the_Memory_Barrier_of_Contrastive_Loss_via_Tile-Based_Strategy_CVPR_2025_paper.html

abstract:
Contrastive loss is a powerful approach for representation learning, where larger batch sizes enhance performance by providing more negative samples to better distinguish between similar and dissimilar data. However, the full instantiation of the similarity matrix demands substantial GPU memory, making large batch training highly resource-intensive. To address this, we propose a tile-based computation strategy that partitions the contrastive loss calculation into small blocks, avoiding full materialization of the similarity matrix.  Additionally, we introduce a multi-level tiling implementation to leverage the hierarchical structure of distributed systems, using ring-based communication at the GPU level to optimize synchronization and fused kernels at the CUDA core level to reduce I/O overhead. Experimental results show that the proposed method significantly reduces GPU memory usage in contrastive loss. For instance, it enables contrastive training of a CLIP-ViT-L/14 model with a batch size of 4M using only 8 A800 80GB GPUs, without sacrificing accuracy. Compared to state-of-the-art memory-efficient solutions, it achieves a two-order-of-magnitude reduction in memory while maintaining comparable speed. The code will be made publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cheng_Breaking_the_Memory_Barrier_of_Contrastive_Loss_via_Tile-Based_Strategy_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cheng_Breaking_the_Memory_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Cheng_2025_CVPR,
    author    = {Cheng, Zesen and Zhang, Hang and Li, Kehan and Leng, Sicong and Hu, Zhiqiang and Wu, Fei and Zhao, Deli and Li, Xin and Bing, Lidong},
    title     = {Breaking the Memory Barrier of Contrastive Loss via Tile-Based Strategy},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10036-10045}
}

raw_affiliation:
[{"affiliation": "Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Shanda AI Research Institute", "city": "Unknown", "country": "Unknown"}]


title: Mimir: Improving Video Diffusion Models for Precise Text Understanding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23978-23988
affiliations: ["Ant Group", "Tsinghua University"]
cities: ["Hangzhou", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tan_Mimir_Improving_Video_Diffusion_Models_for_Precise_Text_Understanding_CVPR_2025_paper.html

authors:
Shuai Tan, Biao Gong, Yutong Feng, Kecheng Zheng, Dandan Zheng, Shuwei Shi, Yujun Shen, Jingdong Chen, Ming Yang

abstract:
Text serves as the key control signal in video generation due to its narrative nature. To render text descriptions into video clips, current video diffusion models borrow features from text encoders yet struggle with limited text comprehension. The recent success of large language models (LLMs) showcases the power of decoder-only transformers, which offers three clear benefits for text-to-video (T2V) generation, namely, precise text understanding resulting from the superior scalability, imagination beyond the input text enabled by next token prediction, and flexibility to prioritize user interests through instruction tuning. Nevertheless, the feature distribution gap emerging from the two different text modeling paradigms hinders the direct use of LLMs in established T2V models. This work addresses this challenge with Mimir, an end-to-end training framework featuring a carefully tailored token fuser to harmonize the outputs from text encoders and LLMs. Such a design allows the T2V model to fully leverage learned video priors while capitalizing on the text-related capability of LLMs. Extensive quantitative and qualitative results demonstrate the effectiveness of our approach in generating high-quality videos with excellent text comprehension, especially when processing short captions and managing shifting motions. The code and models will be made publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tan_Mimir_Improving_Video_Diffusion_Models_for_Precise_Text_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tan_Mimir_Improving_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tan_2025_CVPR,
    author    = {Tan, Shuai and Gong, Biao and Feng, Yutong and Zheng, Kecheng and Zheng, Dandan and Shi, Shuwei and Shen, Yujun and Chen, Jingdong and Yang, Ming},
    title     = {Mimir: Improving Video Diffusion Models for Precise Text Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23978-23988}
}

raw_affiliation:
[{"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]


authors: Xingyue Liu, Jiahao Qi, Chen Chen, KangCheng Bin, Ping Zhong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22286-22295
affiliations: ["National University of Defense Technology, China"]
cities: ["Changsha"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_UCM-VeID_V2_A_Richer_Dataset_and_A_Pre-training_Method_for_CVPR_2025_paper.html

title:
UCM-VeID V2: A Richer Dataset and A Pre-training Method for UAV Cross-Modality Vehicle Re-Identification

abstract:
Cross-Modality Re-Identification (VI-ReID) aims to achieve around-the-clock target matching, benefiting from the strengths of both  RGB and infrared (IR) modalities. However, the field is hindered by limited datasets, particularly for vehicle VI-ReID, and by challenges such as modality bias training (MBT), stemming from biased pre-training on ImageNet. To tackle the above issues, this paper introduces an UCM-VeID V2 dataset benchmark for vehicle VI-ReID, and proposes a new self-supervised pre-training method, Cross-Modality Patch-Mixed Self-supervised Learning (PMSL). UCM-VeID V2 dataset features a significant increase in data volume, along with enhancements in multiple aspects. PMSL addresses MBT by learning modality-invariant features through Patch-Mixed Image Reconstruction (PMIR) and Modality Discrimination Adversarial Learning (MDAL), and enhances discriminability with Modality-Augmented Contrasting Cluster (MACC).  Comprehensive experiments are carried out to validate the effectiveness of the proposed method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_UCM-VeID_V2_A_Richer_Dataset_and_A_Pre-training_Method_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_UCM-VeID_V2_A_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Xingyue and Qi, Jiahao and Chen, Chen and Bin, KangCheng and Zhong, Ping},
    title     = {UCM-VeID V2: A Richer Dataset and A Pre-training Method for UAV Cross-Modality Vehicle Re-Identification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22286-22295}
}

raw_affiliation:
[{"affiliation": "National University of Defense Technology, China", "city": "Changsha", "country": "China"}]


title: Learning Phase Distortion with Selective State Space Models for Video Turbulence Mitigation
authors: Xingguang Zhang, Nicholas Chimitt, Xijun Wang, Yu Yuan, Stanley H. Chan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2127-2138
affiliations: ["School of Electrical and Computer Engineering, Purdue University"]
cities: ["West Lafayette"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Learning_Phase_Distortion_with_Selective_State_Space_Models_for_Video_CVPR_2025_paper.html

abstract:
Atmospheric turbulence is a major source of image degradation in long-range imaging systems. Although numerous deep learning-based turbulence mitigation (TM) methods have been proposed, many are slow, memory-hungry, and do not generalize well. In the spatial domain, methods based on convolutional operators have a limited receptive field, so they cannot handle a large spatial dependency required by turbulence. In the temporal domain, methods relying on self-attention can, in theory, leverage the lucky effects of turbulence, but their quadratic complexity makes it difficult to scale to many frames. Traditional recurrent aggregation methods face parallelization challenges. In this paper, we present a new TM method based on two concepts: (1) A turbulence mitigation network based on the Selective State Space Model (MambaTM). MambaTM provides a global receptive field in each layer across spatial and temporal dimensions while maintaining linear computational complexity. (2) Learned Latent Phase Distortion (LPD). LPD guides the state space model. Unlike classical Zernike-based representations of phase distortion, the new LPD map uniquely captures the actual effects of turbulence, significantly improving the model's capability to estimate degradation by reducing the ill-posedness. Our proposed method exceeds current state-of-the-art networks on various synthetic and real-world TM benchmarks with significantly faster inference speed. The code will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Learning_Phase_Distortion_with_Selective_State_Space_Models_for_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Learning_Phase_Distortion_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Xingguang and Chimitt, Nicholas and Wang, Xijun and Yuan, Yu and Chan, Stanley H.},
    title     = {Learning Phase Distortion with Selective State Space Models for Video Turbulence Mitigation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2127-2138}
}

raw_affiliation:
[{"affiliation": "School of Electrical and Computer Engineering, Purdue University", "city": "West Lafayette", "country": "USA"}]


authors: Raktim Gautam Goswami, Prashanth Krishnamurthy, Yann LeCun, Farshad Khorrami
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6930-6939
cities: ["New York", "New York", "Menlo Park"]
countries: ["USA", "USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Goswami_RoboPEPP_Vision-Based_Robot_Pose_and_Joint_Angle_Estimation_through_Embedding_CVPR_2025_paper.html

title:
RoboPEPP: Vision-Based Robot Pose and Joint Angle Estimation through Embedding Predictive Pre-Training

abstract:
Vision-based pose estimation of articulated robots with unknown joint angles has applications in collaborative robotics and human-robot interaction tasks. Current frameworks use neural network encoders to extract image features and downstream layers to predict joint angles and robot pose. While images of robots inherently contain rich information about the robot's physical structures, existing methods often fail to leverage it fully; therefore, limiting performance under occlusions and truncations. To address this, we introduce RoboPEPP, a method that fuses information about the robot's physical model into the encoder using a masking-based self-supervised embedding-predictive architecture. Specifically, we mask the robot's joints and pre-train an encoder-predictor model to infer the joints' embeddings from surrounding unmasked regions, enhancing the encoder's understanding of the robot's physical model. The pre-trained encoder-predictor pair, along with joint angle and keypoint prediction networks, is then fine-tuned for pose and joint angle estimation. Random masking of input during fine-tuning and keypoint filtering during evaluation further improves robustness. Our method, evaluated on several datasets, achieves the best results in robot pose and joint angle estimation while being the least sensitive to occlusions and requiring the lowest execution time.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Goswami_RoboPEPP_Vision-Based_Robot_Pose_and_Joint_Angle_Estimation_through_Embedding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Goswami_RoboPEPP_Vision-Based_Robot_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Goswami_2025_CVPR,
    author    = {Goswami, Raktim Gautam and Krishnamurthy, Prashanth and LeCun, Yann and Khorrami, Farshad},
    title     = {RoboPEPP: Vision-Based Robot Pose and Joint Angle Estimation through Embedding Predictive Pre-Training},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6930-6939}
}

raw_affiliation:
[{"affiliation": "New York University Tandon School of Engineering", "city": "New York", "country": "USA"}, {"affiliation": "New York University Courant Institute of Mathematical Sciences", "city": "New York", "country": "USA"}, {"affiliation": "Meta-FAIR", "city": "Menlo Park", "country": "USA"}]

affiliations:
["New York University Tandon School of Engineering", "New York University Courant Institute of Mathematical Sciences", "Meta-FAIR"]


title: Distraction is All You Need for Multimodal Large Language Model Jailbreaking
authors: Zuopeng Yang, Jiluan Fan, Anli Yan, Erdun Gao, Xin Lin, Tao Li, Kanghua Mo, Changyu Dong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9467-9476
cities: ["Guangzhou", "Shanghai", "Adelaide"]
countries: ["China", "China", "Australia"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Distraction_is_All_You_Need_for_Multimodal_Large_Language_Model_CVPR_2025_paper.html

abstract:
Multimodal Large Language Models (MLLMs) bridge the gap between visual and textual data, enabling a range of advanced applications. However, complex internal interactions among visual elements and their alignment with text can introduce vulnerabilities, which may be exploited to bypass safety mechanisms. To address this, we analyze the relationship between image content and task and find that the complexity of subimages, rather than their content, is key. Building on this insight, we propose the Distraction Hypothesis, followed by a novel framework called Contrasting Subimage Distraction Jailbreaking (CS-DJ), to achieve jailbreaking by disrupting MLLMs alignment through multi-level distraction strategies. CS-DJ consists of two components: structured distraction, achieved through query decomposition that induces a distributional shift by fragmenting harmful prompts into sub-queries, and visual-enhanced distraction, realized by constructing contrasting subimages to disrupt the interactions among visual elements within the model. This dual strategy disperses the model's attention, reducing its ability to detect and mitigate harmful content. Extensive experiments across five representative scenarios and four popular closed-source MLLMs, including \texttt GPT-4o-mini , \texttt GPT-4o , \texttt GPT-4V , and \texttt Gemini-1.5-Flash , demonstrate that CS-DJ achieves average success rates of 52.40% for the attack success rate and 74.10% for the ensemble attack success rate. These results reveal the potential of distraction-based approaches to exploit and bypass MLLMs' defenses, offering new insights for attack strategies.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Distraction_is_All_You_Need_for_Multimodal_Large_Language_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Distraction_is_All_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Zuopeng and Fan, Jiluan and Yan, Anli and Gao, Erdun and Lin, Xin and Li, Tao and Mo, Kanghua and Dong, Changyu},
    title     = {Distraction is All You Need for Multimodal Large Language Model Jailbreaking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9467-9476}
}

raw_affiliation:
[{"affiliation": "Guangzhou University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Australian Institute for Machine Learning, The University of Adelaide", "city": "Adelaide", "country": "Australia"}]

affiliations:
["Guangzhou University", "Shanghai Jiao Tong University", "Australian Institute for Machine Learning, The University of Adelaide"]


title: Apollo: An Exploration of Video Understanding in Large Multimodal Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18891-18901
affiliations: ["Meta GenAI", "Stanford University", "Work done at Meta"]
cities: ["Menlo Park", "Stanford", "Menlo Park"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zohar_Apollo__An_Exploration_of_Video_Understanding_in_Large_Multimodal_CVPR_2025_paper.html

authors:
Orr Zohar, Xiaohan Wang, Yann Dubois, Nikhil Mehta, Tong Xiao, Philippe Hansen-Estruch, Licheng Yu, Xiaofang Wang, Felix Juefei-Xu, Ning Zhang, Serena Yeung-Levy, Xide Xia

abstract:
Despite the rapid integration of video perception capabilities into Large Multimodal Models (LMMs), what drives their video perception remains poorly understood. Consequently, many design decisions in this domain are made without proper justification or analysis. The high computational cost of training and evaluating such models and limited open research hinder the development of video-LMMs. To address this, we present a comprehensive study that helps uncover what effectively drives video understanding in LMMs. We begin by critically examining the primary contributors to the high computational requirements associated with video-LMM research and discover Scaling Consistency, wherein design and training decisions made on smaller models and datasets (up to a critical size) effectively transfer to larger models. Leveraging these insights, we explored many video-specific aspects of video-LMMs, including video sampling, architectures, data composition, training schedules, and more. Guided by these findings, we introduce Apollo, a state-of-the-art family of LMMs that achieve superior performance across different model sizes. Our models process over 1-hour videos efficiently, with the 3B parameter variant outperforming most existing 7B models. Apollo-7B is state-of-the-art compared to 7B LMMs with a 70.9 on MLVU, and 63.3 on Video-MME.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zohar_Apollo__An_Exploration_of_Video_Understanding_in_Large_Multimodal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zohar_Apollo__An_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zohar_2025_CVPR,
    author    = {Zohar, Orr and Wang, Xiaohan and Dubois, Yann and Mehta, Nikhil and Xiao, Tong and Hansen-Estruch, Philippe and Yu, Licheng and Wang, Xiaofang and Juefei-Xu, Felix and Zhang, Ning and Yeung-Levy, Serena and Xia, Xide},
    title     = {Apollo:  An Exploration of Video Understanding in Large Multimodal Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18891-18901}
}

raw_affiliation:
[{"affiliation": "Meta GenAI", "city": "Menlo Park", "country": "USA"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "Work done at Meta", "city": "Menlo Park", "country": "USA"}]


title: Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves
authors: Shihan Wu, Ji Zhang, Pengpeng Zeng, Lianli Gao, Jingkuan Song, Heng Tao Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14723-14732
cities: ["Chengdu", "Chengdu", "Shenzhen", "Shanghai"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Skip_Tuning_Pre-trained_Vision-Language_Models_are_Effective_and_Efficient_Adapters_CVPR_2025_paper.html

abstract:
Prompt tuning (PT) has long been recognized as an effective and efficient paradigm for transferring large pre-trained vision-language models (VLMs) to downstream tasks by learning a tiny set of context vectors. Nevertheless, in this work, we reveal that freezing the parameters of VLMs during learning the context vectors neither facilitates the transferability of pre-trained knowledge nor improves the memory and time efficiency significantly. Upon further investigation, we find that reducing both the length and width of the feature-gradient propagation flows of the full fine-tuning (FT) baseline is key to achieving effective and efficient knowledge transfer. Motivated by this, we propose Skip Tuning, a novel paradigm for adapting VLMs to downstream tasks. Unlike existing PT or adapter-based methods, Skip Tuning applies Layer-wise Skipping (LSkip) and Class-wise Skipping (CSkip) upon the FT baseline without introducing extra context vectors or adapter modules. Extensive experiments across a wide spectrum of benchmarks demonstrate the superior effectiveness and efficiency of our Skip Tuning over both PT and adapter-based methods. Code: https://github.com/anonymity-007/SkipT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Skip_Tuning_Pre-trained_Vision-Language_Models_are_Effective_and_Efficient_Adapters_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Shihan and Zhang, Ji and Zeng, Pengpeng and Gao, Lianli and Song, Jingkuan and Shen, Heng Tao},
    title     = {Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14723-14732}
}

raw_affiliation:
[{"affiliation": "University of Electronic Science and Technology of China (UESTC)", "city": "Chengdu", "country": "China"}, {"affiliation": "Southwest Jiaotong University", "city": "Chengdu", "country": "China"}, {"affiliation": "Shenzhen Institute for Advanced Study, UESTC", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tongji University", "city": "Shanghai", "country": "China"}]

affiliations:
["University of Electronic Science and Technology of China (UESTC)", "Southwest Jiaotong University", "Shenzhen Institute for Advanced Study, UESTC", "Tongji University"]


title: PatchDPO: Patch-level DPO for Finetuning-free Personalized Image Generation
authors: Qihan Huang, Long Chan, Jinlong Liu, Wanggui He, Hao Jiang, Mingli Song, Jie Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18369-18378
affiliations: ["Zhejiang University", "Alibaba Group"]
cities: ["Hangzhou", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_PatchDPO_Patch-level_DPO_for_Finetuning-free_Personalized_Image_Generation_CVPR_2025_paper.html

abstract:
Finetuning-free personalized image generation can synthesize customized images without test-time finetuning, attracting wide research interest owing to its high efficiency. Current finetuning-free methods simply adopt a single training stage with a simple image reconstruction task, and they typically generate low-quality images inconsistent with the reference images during test-time. To mitigate this problem, inspired by the recent DPO (i.e., direct preference optimization) technique, this work proposes an additional training stage to improve the pre-trained personalized generation models. However, traditional DPO only determines the overall superiority or inferiority of two samples, which is not suitable for personalized image generation because the generated images are commonly inconsistent with the reference images only in some local image patches. To tackle this problem, this work proposes PatchDPO that estimates the quality of image patches within each generated image and accordingly trains the model. To this end, PatchDPO first leverages the pre-trained vision models with a proposed self-supervised training method to estimate the patch quality. Next, PatchDPO adopts a weighted training approach to train the model with the estimated patch quality, which rewards the image patches with high quality while penalizing the image patches with low quality. Experiment results demonstrate that PatchDPO significantly improves the performance of multiple pre-trained personalized generation models, and achieves state-of-the-art performance on both single-object and multi-object personalized image generation. Our code is available at https://github.com/hqhQAQ/PatchDPO.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_PatchDPO_Patch-level_DPO_for_Finetuning-free_Personalized_Image_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_PatchDPO_Patch-level_DPO_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Qihan and Chan, Long and Liu, Jinlong and He, Wanggui and Jiang, Hao and Song, Mingli and Song, Jie},
    title     = {PatchDPO: Patch-level DPO for Finetuning-free Personalized Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18369-18378}
}

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


title: Learning to Normalize on the SPD Manifold under Bures-Wasserstein Geometry
authors: Rui Wang, Shaocheng Jin, Ziheng Chen, Xiaoqing Luo, Xiao-Jun Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8289-8298
cities: ["Wuxi", "Trento"]
countries: ["China", "Italy"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Learning_to_Normalize_on_the_SPD_Manifold_under_Bures-Wasserstein_Geometry_CVPR_2025_paper.html

abstract:
Covariance matrices have proven highly effective across many scientific fields. Since these matrices lie within the Symmetric Positive Definite (SPD) manifold--a Riemannian space with intrinsic non-Euclidean geometry, the primary challenge in representation learning is to respect this underlying geometric structure. Drawing inspiration from the success of Euclidean deep learning, researchers have developed neural networks on the SPD manifolds for more faithful covariance embedding learning. A notable advancement in this area is the implementation of Riemannian batch normalization (RBN), which has been shown to improve the performance of SPD network models. Nonetheless, the Riemannian metric beneath the existing RBN might fail to effectively deal with the ill-conditioned SPD matrices (ICSM), undermining the effectiveness of RBN. In contrast, the Bures-Wasserstein metric (BWM) demonstrates superior performance for ill-conditioning. In addition, the recently introduced Generalized BWM (GBWM) parameterizes the vanilla BWM via an SPD matrix, allowing for a more nuanced representation of vibrant geometries of the SPD manifold. Therefore, we propose a novel RBN algorithm based on the GBW geometry, incorporating a learnable metric parameter. Moreover, the deformation of GBWM by matrix power is also introduced to further enhance the representational capacity of GBWM-based RBN. Experimental results on different datasets validate the effectiveness of our proposed method. The code is available at https://github.com/jjscc/GBWBN.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Learning_to_Normalize_on_the_SPD_Manifold_under_Bures-Wasserstein_Geometry_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Learning_to_Normalize_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Rui and Jin, Shaocheng and Chen, Ziheng and Luo, Xiaoqing and Wu, Xiao-Jun},
    title     = {Learning to Normalize on the SPD Manifold under Bures-Wasserstein Geometry},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8289-8298}
}

raw_affiliation:
[{"affiliation": "School of Arti\ufb01cial Intelligence and Computer Science, Jiangnan University", "city": "Wuxi", "country": "China"}, {"affiliation": "Department of Information Engineering and Computer Science, University of Trento", "city": "Trento", "country": "Italy"}]

affiliations:
["School of Arti\ufb01cial Intelligence and Computer Science, Jiangnan University", "Department of Information Engineering and Computer Science, University of Trento"]


title: SAMWISE: Infusing Wisdom in SAM2 for Text-Driven Video Segmentation
authors: Claudia Cuttano, Gabriele Trivigno, Gabriele Rosi, Carlo Masone, Giuseppe Averta
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3395-3405
affiliations: ["Politecnico di Torino", "Focoos AI"]
cities: ["Turin", "Turin"]
countries: ["Italy", "Italy"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cuttano_SAMWISE_Infusing_Wisdom_in_SAM2_for_Text-Driven_Video_Segmentation_CVPR_2025_paper.html

abstract:
Referring Video Object Segmentation (RVOS) relies on natural language expressions to segment an object in a video clip. Existing methods restrict reasoning either to independent short clips, losing global context, or process the entire video offline, impairing their application in a streaming fashion. In this work, we aim to surpass these limitations and design an RVOS method capable of effectively operating in streaming-like scenarios while retaining contextual information from past frames. We build upon the Segment-Anything 2 (SAM2) model, that provides robust segmentation and tracking capabilities and is naturally suited for streaming processing. We make SAM2 wiser, by empowering it with natural language understanding and explicit temporal modeling  at the feature extraction stage, without fine-tuning its weights, and without outsourcing modality interaction to external models. To this end, we introduce a novel adapter module that injects temporal information and multi-modal cues in the feature extraction process. We further reveal the phenomenon of tracking bias in SAM2 and propose a learnable module to adjust its tracking focus when the current frame features suggest a new object more aligned with the caption. Our proposed method, SAMWISE, achieves state-of-the-art across various benchmarks, by adding a negligible overhead of less than 5 M parameters. Code is available at https://github.com/ClaudiaCuttano/SAMWISE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cuttano_SAMWISE_Infusing_Wisdom_in_SAM2_for_Text-Driven_Video_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cuttano_SAMWISE_Infusing_Wisdom_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cuttano_2025_CVPR,
    author    = {Cuttano, Claudia and Trivigno, Gabriele and Rosi, Gabriele and Masone, Carlo and Averta, Giuseppe},
    title     = {SAMWISE: Infusing Wisdom in SAM2 for Text-Driven Video Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3395-3405}
}

raw_affiliation:
[{"affiliation": "Politecnico di Torino", "city": "Turin", "country": "Italy"}, {"affiliation": "Focoos AI", "city": "Turin", "country": "Italy"}]


title: MegaSaM: Accurate, Fast and Robust Structure and Motion from Casual Dynamic Videos
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10486-10496
affiliations: ["Google DeepMind", "UC Berkeley", "University of Michigan"]
cities: ["Mountain View", "Berkeley", "Ann Arbor"]
countries: ["USA", "USA", "USA"]
oral: yes
award_candidate: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_MegaSaM_Accurate_Fast_and_Robust_Structure_and_Motion_from_Casual_CVPR_2025_paper.html

authors:
Zhengqi Li, Richard Tucker, Forrester Cole, Qianqian Wang, Linyi Jin, Vickie Ye, Angjoo Kanazawa, Aleksander Holynski, Noah Snavely

abstract:
We present a system that allows for accurate, fast, and robust estimation of camera parameters and depth maps from casual monocular videos of dynamic scenes. Most conventional structure from motion and monocular SLAM techniques assume input videos that feature predominantly static scenes with large amounts of parallax. Such methods tend to produce erroneous estimates in the absence of these conditions. Recent neural network based approaches attempt to overcome these challenges; however, such methods are either computationally expensive or brittle when run on dynamic videos with uncontrolled camera motion or unknown field of view. We demonstrate the surprising effectiveness of the deep visual SLAM framework, and with careful modifications to its training and inference schemes, this system can scale to real-world videos of complex dynamic scenes with unconstrained camera paths, including videos with little camera parallax. Extensive experiments on both synthetic and real videos demonstrate that our system is significantly more accurate and robust at camera pose and depth estimation when compared with prior and concurrent work, with faster or comparable running times.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_MegaSaM_Accurate_Fast_and_Robust_Structure_and_Motion_from_Casual_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_MegaSaM_Accurate_Fast_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Zhengqi and Tucker, Richard and Cole, Forrester and Wang, Qianqian and Jin, Linyi and Ye, Vickie and Kanazawa, Angjoo and Holynski, Aleksander and Snavely, Noah},
    title     = {MegaSaM: Accurate, Fast and Robust Structure and Motion from Casual Dynamic Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10486-10496}
}

raw_affiliation:
[{"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}, {"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}]


title: BEVDiffuser: Plug-and-Play Diffusion Model for BEV Denoising with Ground-Truth Guidance
authors: Xin Ye, Burhaneddin Yaman, Sheng Cheng, Feng Tao, Abhirup Mallik, Liu Ren
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1495-1504
affiliations: ["Bosch Research North America & Bosch Center for Arti\ufb01cial Intelligence (BCAI)"]
cities: ["Palo Alto"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ye_BEVDiffuser_Plug-and-Play_Diffusion_Model_for_BEV_Denoising_with_Ground-Truth_Guidance_CVPR_2025_paper.html

abstract:
Bird's-eye-view (BEV) representations play a crucial role in autonomous driving tasks. Despite recent advancements in BEV generation, inherent noise, stemming from sensor limitations and the learning process, remains largely unaddressed, resulting in suboptimal BEV representations that adversely impact the performance of downstream tasks. To address this, we propose BEVDiffuser, a novel diffusion model that effectively denoises BEV feature maps using the ground-truth object layout as guidance.  BEVDiffuser can be operated in a plug-and-play manner during training time to enhance existing BEV models without requiring any architectural modifications. Extensive experiments on the challenging nuScenes dataset demonstrate BEVDiffuser's exceptional denoising and generation capabilities, which enable significant enhancement to existing BEV models, as evidenced by notable improvements of 12.3% in mAP and 10.1% in NDS achieved for 3D object detection without introducing additional computational complexity. Moreover, substantial improvements in long-tail object detection and under challenging weather and lighting conditions further validate BEVDiffuser's effectiveness in denoising and enhancing BEV representations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ye_BEVDiffuser_Plug-and-Play_Diffusion_Model_for_BEV_Denoising_with_Ground-Truth_Guidance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ye_BEVDiffuser_Plug-and-Play_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ye_2025_CVPR,
    author    = {Ye, Xin and Yaman, Burhaneddin and Cheng, Sheng and Tao, Feng and Mallik, Abhirup and Ren, Liu},
    title     = {BEVDiffuser: Plug-and-Play Diffusion Model for BEV Denoising with Ground-Truth Guidance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1495-1504}
}

raw_affiliation:
[{"affiliation": "Bosch Research North America & Bosch Center for Arti\ufb01cial Intelligence (BCAI)", "city": "Palo Alto", "country": "USA"}]


authors: Soohyun Lee, Seoyeon Kim, HeeKyung Lee, Won-Sik Jeong, Joo Ho Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21138-21147
affiliations: ["Sogang University", "Electronics and Telecommunications Research Institute"]
cities: ["Seoul", "Daejeon"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_GeoAvatar_Geometrically-Consistent_Multi-Person_Avatar_Reconstruction_from_Sparse_Multi-View_Videos_CVPR_2025_paper.html

title:
GeoAvatar: Geometrically-Consistent Multi-Person Avatar Reconstruction from Sparse Multi-View Videos

abstract:
Multi-person avatar reconstruction from sparse multi-view videos is challenging. The independent avatar reconstruction of each person often fails to reconstruct the geometric relationship among multiple instances, resulting in inter-penetrations among avatars. Some researchers resolve this issue via neural volumetric rendering techniques but they suffer from huge computational costs for rendering and training. In this paper, we propose a multi-person avatar reconstruction method that reconstructs a 3D avatar of each person while keeping the geometric relations among people. Our 2D Gaussian Splatting (2DGS)-based avatar representation allows us to represent geometrically-accurate surfaces of multiple instances that support sharp inside-outside tests. We utilize the monocular prior to alleviate the inter-penetration via surface ordering and to enhance the geometry in less-observed and textureless surfaces. We demonstrate the efficiency and performance of our method quantitatively and qualitatively on a multi-person dataset containing close interactions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_GeoAvatar_Geometrically-Consistent_Multi-Person_Avatar_Reconstruction_from_Sparse_Multi-View_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_GeoAvatar_Geometrically-Consistent_Multi-Person_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Soohyun and Kim, Seoyeon and Lee, HeeKyung and Jeong, Won-Sik and Lee, Joo Ho},
    title     = {GeoAvatar: Geometrically-Consistent Multi-Person Avatar Reconstruction from Sparse Multi-View Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21138-21147}
}

raw_affiliation:
[{"affiliation": "Sogang University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Electronics and Telecommunications Research Institute", "city": "Daejeon", "country": "South Korea"}]


authors: Dian Shao, Mingfei Shi, Shengda Xu, Haodong Chen, Yongle Huang, Binglu Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1905-1916
cities: ["Xi'an", "Xi'an", "Xi'an", "Xi'an"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shao_FinePhys_Fine-grained_Human_Action_Generation_by_Explicitly_Incorporating_Physical_Laws_CVPR_2025_paper.html

title:
FinePhys: Fine-grained Human Action Generation by Explicitly Incorporating Physical Laws for Effective Skeletal Guidance

abstract:
Although remarkable progress has been achieved in video generation, synthesizing physically plausible human actions remains an unresolved challenge, especially when addressing fine-grained semantics and complex temporal dynamics. For instance, generating gymnastics routines such as "two turns on one leg with the free leg optionally below horizontal" poses substantial difficulties for current video generation methods, which often fail to produce satisfactory results. To address this, we propose FinePhys, a Fine-grained human action generation framework incorporating Physics for effective skeletal guidance. Specifically, FinePhys first performs online 2D pose estimation and then accomplishes dimension lifting through in-context learning. Recognizing that such data-driven 3D pose estimations may lack stability and interpretability, we incorporate a physics-based module that re-estimates motion dynamics using Euler-Lagrange equations, calculating joint accelerations bidirectionally across the temporal dimension. The physically predicted 3D poses are then fused with data-driven poses to provide multi-scale 2D heatmap-based guidance for the video generation process. Evaluated on three fine-grained action subsets from FineGym (FX-JUMP, FX-TURN, and FX-SALTO), FinePhys significantly outperforms competitive baselines. Comprehensive qualitative results further demonstrate FinePhys's ability to generate more natural and plausible fine-grained human actions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shao_FinePhys_Fine-grained_Human_Action_Generation_by_Explicitly_Incorporating_Physical_Laws_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shao_FinePhys_Fine-grained_Human_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shao_2025_CVPR,
    author    = {Shao, Dian and Shi, Mingfei and Xu, Shengda and Chen, Haodong and Huang, Yongle and Wang, Binglu},
    title     = {FinePhys: Fine-grained Human Action Generation by Explicitly Incorporating Physical Laws for Effective Skeletal Guidance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1905-1916}
}

raw_affiliation:
[{"affiliation": "Unmanned System Research Institute, Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "School of Software, Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "School of Automation, Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "School of Astronautics, Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}]

affiliations:
["Unmanned System Research Institute, Northwestern Polytechnical University", "School of Software, Northwestern Polytechnical University", "School of Automation, Northwestern Polytechnical University", "School of Astronautics, Northwestern Polytechnical University"]


title: DiET-GS: Diffusion Prior and Event Stream-Assisted Motion Deblurring 3D Gaussian Splatting
authors: Seungjun Lee, Gim Hee Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21739-21749
affiliations: ["Department of Computer Science, National University of Singapore"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_DiET-GS_Diffusion_Prior_and_Event_Stream-Assisted_Motion_Deblurring_3D_Gaussian_CVPR_2025_paper.html

abstract:
Reconstructing sharp 3D representations from blurry multi-view images are long-standing problem in computer vision. Recent works attempt to enhance high-quality novel view synthesis from the motion blur by leveraging event-based cameras, benefiting from high dynamic range and microsecond temporal resolution. However, they often reach sub-optimal visual quality in either restoring inaccurate color or losing fine-grained details. In this paper, we present DiET-GS, a diffusion prior and event stream-assisted motion deblurring 3DGS. Our framework effectively leverages blur-free event streams and diffusion prior in a two-stage training strategy. Specifically, we introduce the novel framework to constraint 3DGS with event double integral, achieving both accurate color and well-defined details. Additionally, we propose a simple technique to leverage diffusion prior to further enhance the edge details. Qualitative and quantitative results on both synthetic and real-world data demonstrate that our DiET-GS is capable of producing better quality of novel views compared to the existing baselines. The project page link is attached in main paper.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_DiET-GS_Diffusion_Prior_and_Event_Stream-Assisted_Motion_Deblurring_3D_Gaussian_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_DiET-GS_Diffusion_Prior_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Seungjun and Lee, Gim Hee},
    title     = {DiET-GS: Diffusion Prior and Event Stream-Assisted Motion Deblurring 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21739-21749}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science, National University of Singapore", "city": "Singapore", "country": "Singapore"}]


title: Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives
authors: Alex Hanson, Allen Tu, Geng Lin, Vasu Singla, Matthias Zwicker, Tom Goldstein
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21537-21546
raw_affiliation: [{"affiliation": "University of Maryland", "city": "College Park", "country": "USA"}]
affiliations: ["University of Maryland"]
cities: ["College Park"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hanson_Speedy-Splat_Fast_3D_Gaussian_Splatting_with_Sparse_Pixels_and_Sparse_CVPR_2025_paper.html

abstract:
3D Gaussian Splatting (3D-GS) is a recent 3D scene reconstruction technique that enables real-time rendering of novel views by modeling scenes as parametric point clouds of differentiable 3D Gaussians. However, its rendering speed and model size still present bottlenecks, especially in resource-constrained settings. In this paper, we identify and address two key inefficiencies in 3D-GS to substantially improve rendering speed.These improvements also yield the ancillary benefits of reduced model size and training time. First, we optimize the rendering pipeline to precisely localize Gaussians in the scene, boosting rendering speed without altering visual fidelity. Second, we introduce a novel pruning technique and integrate it into the training pipeline, significantly reducing model size and training time while further raising rendering speed. Our Speedy-Splat approach combines these techniques to accelerate average rendering speed by a drastic 6.71x across scenes from the Mip-NeRF 360, Tanks&Temples, and Deep Blending datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hanson_Speedy-Splat_Fast_3D_Gaussian_Splatting_with_Sparse_Pixels_and_Sparse_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hanson_Speedy-Splat_Fast_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hanson_2025_CVPR,
    author    = {Hanson, Alex and Tu, Allen and Lin, Geng and Singla, Vasu and Zwicker, Matthias and Goldstein, Tom},
    title     = {Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21537-21546}
}


title: SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video Restoration
authors: Jianyi Wang, Zhijie Lin, Meng Wei, Yang Zhao, Ceyuan Yang, Chen Change Loy, Lu Jiang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2161-2172
affiliations: ["Nanyang Technological University", "ByteDance"]
cities: ["Singapore", "Beijing"]
countries: ["Singapore", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_SeedVR_Seeding_Infinity_in_Diffusion_Transformer_Towards_Generic_Video_Restoration_CVPR_2025_paper.html

abstract:
Video restoration poses non-trivial challenges in maintaining fidelity while recovering temporally consistent details from unknown degradations in the wild. Despite recent advances in diffusion-based restoration, these methods often face limitations in generation capability and sampling efficiency. In this work, we present \text SeedVR , a diffusion transformer designed to handle real-world video restoration with arbitrary length and resolution. The core design of SeedVR lies in the shifted window attention that facilitates effective restoration on long video sequences. SeedVR further supports variable-sized windows near the boundary of both spatial and temporal dimensions, overcoming the resolution constraints of traditional window attention. Equipped with contemporary practices, including causal video autoencoder, mixed image and video training, and progressive training, SeedVR achieves highly-competitive performance on both synthetic and real-world benchmarks, as well as AI-generated videos. Extensive experiments demonstrate SeedVR's superiority over existing methods for generic video restoration.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_SeedVR_Seeding_Infinity_in_Diffusion_Transformer_Towards_Generic_Video_Restoration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_SeedVR_Seeding_Infinity_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Jianyi and Lin, Zhijie and Wei, Meng and Zhao, Yang and Yang, Ceyuan and Loy, Chen Change and Jiang, Lu},
    title     = {SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2161-2172}
}

raw_affiliation:
[{"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "ByteDance", "city": "Beijing", "country": "China"}]


title: Beyond Generation: A Diffusion-based Low-level Feature Extractor for Detecting AI-generated Images
authors: Nan Zhong, Haoyu Chen, Yiran Xu, Zhenxing Qian, Xinpeng Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8258-8268
affiliations: ["City University of Hong Kong", "Fudan University"]
cities: ["Hong Kong", "Shanghai"]
countries: ["Hong Kong", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhong_Beyond_Generation_A_Diffusion-based_Low-level_Feature_Extractor_for_Detecting_AI-generated_CVPR_2025_paper.html

abstract:
The prevalence of AI-generated images has evoked concerns regarding the potential misuse of image generation technologies. In response, numerous detection methods aim to identify AI-generated images by analyzing generative artifacts. Unfortunately, most detectors quickly become obsolete with the development of generative models. In this paper, we first design a low-level feature extractor that transforms spatial images into feature space, where different source images exhibit distinct distributions. The pretext task for the feature extractor is to distinguish between images that differ only at the pixel level. This image set comprises the original image as well as versions that have been subjected to varying levels of noise and subsequently denoised using a pre-trained diffusion model. We employ the diffusion model as a denoising tool rather than an image generation tool. Then, we frame the AI-generated image detection task as a one-class classification. We estimate the low-level intrinsic feature distribution of real photographic images and identify features that deviate from this distribution as indicators of AI-generated images. We evaluate our method against over 20 different generative models, including those in GenImage and DRCT-2M datasets. Extensive experiments demonstrate its effectiveness on AI-generated images produced not only by diffusion models but also by GANs, flow-based models, and their variants.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhong_Beyond_Generation_A_Diffusion-based_Low-level_Feature_Extractor_for_Detecting_AI-generated_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhong_Beyond_Generation_A_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhong_2025_CVPR,
    author    = {Zhong, Nan and Chen, Haoyu and Xu, Yiran and Qian, Zhenxing and Zhang, Xinpeng},
    title     = {Beyond Generation: A Diffusion-based Low-level Feature Extractor for Detecting AI-generated Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8258-8268}
}

raw_affiliation:
[{"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "Hong Kong"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}]


title: Robust-MVTON: Learning Cross-Pose Feature Alignment and Fusion for Robust Multi-View Virtual Try-On
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16029-16039
affiliations: ["CUHKSZ", "NJUST", "UCSD", "SYSU", "Westlake University", "USTC"]
cities: ["Shenzhen", "Nanjing", "San Diego", "Guangzhou", "Hangzhou", "Hefei"]
countries: ["China", "China", "USA", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Robust-MVTON_Learning_Cross-Pose_Feature_Alignment_and_Fusion_for_Robust_Multi-View_CVPR_2025_paper.html

authors:
Nannan Zhang, Yijiang Li, Dong Du, Zheng Chong, Zhengwentai Sun, Jianhao Zeng, Yusheng Dai, Zhengyu Xie, Hairui Zhu, Xiaoguang Han

abstract:
This paper tackles the emerging challenge of multi-view virtual try-on, utilizing both front- and back-view clothing images as inputs. Extending frontal try-on methods to a multi-view context is not straightforward. Simply concatenating the two input views or encoding their features for a generative model, such as a diffusion model, often fails to produce satisfactory results. The main challenge lies in effectively extracting and fusing meaningful clothing features from these input views. Existing explicit warping based methods, which establish direct correspondence between input and target views, tend to introduce artifacts, particularly when there is a significant disparity between the input and target views. Conversely, implicit encoding based methods often lose spatial information about clothing, resulting in outputs that lack detail. To overcome these challenges, we propose Robust-MVTON, an end-to end method for robust and high-quality multi-view try-ons. Our approach introduces a novel cross-pose feature alignment technique to guide the fusion of clothing features and incorporates a newly designed loss function for training. With the fused multi-scale clothing features, we employ a coarse-to-fine diffusion model to generate realistic and detailed results. Extensive experiments conducted on the Deepfashion and MPV datasets affirm the superiority of our method, achieving state-of-the-art performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Robust-MVTON_Learning_Cross-Pose_Feature_Alignment_and_Fusion_for_Robust_Multi-View_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Robust-MVTON_Learning_Cross-Pose_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Nannan and Li, Yijiang and Du, Dong and Chong, Zheng and Sun, Zhengwentai and Zeng, Jianhao and Dai, Yusheng and Xie, Zhengyu and Zhu, Hairui and Han, Xiaoguang},
    title     = {Robust-MVTON: Learning Cross-Pose Feature Alignment and Fusion for Robust Multi-View Virtual Try-On},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16029-16039}
}

raw_affiliation:
[{"affiliation": "CUHKSZ", "city": "Shenzhen", "country": "China"}, {"affiliation": "NJUST", "city": "Nanjing", "country": "China"}, {"affiliation": "UCSD", "city": "San Diego", "country": "USA"}, {"affiliation": "SYSU", "city": "Guangzhou", "country": "China"}, {"affiliation": "Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "USTC", "city": "Hefei", "country": "China"}]


title: Omnia de EgoTempo: Benchmarking Temporal Understanding of Multi-Modal LLMs in Egocentric Videos
authors: Chiara Plizzari, Alessio Tonioni, Yongqin Xian, Achin Kulshrestha, Federico Tombari
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24129-24138
affiliations: ["Google", "Politecnico di Torino", "Technical University of Munich"]
cities: ["Mountain View", "Turin", "Munich"]
countries: ["USA", "Italy", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Plizzari_Omnia_de_EgoTempo_Benchmarking_Temporal_Understanding_of_Multi-Modal_LLMs_in_CVPR_2025_paper.html

abstract:
Understanding fine-grained temporal dynamics is crucial in egocentric videos, where continuous streams capture frequent, close-up interactions with objects. In this work, we bring to light that current egocentric video question-answering datasets often include questions that can be answered using only few frames or commonsense reasoning, without being necessarily grounded in the actual video. Our analysis shows that state-of-the-art Multi-Modal Large Language Models (MLLMs) on these benchmarks achieve remarkably high performance using just text or a single frame as input. To address these limitations, we introduce EgoTempo, a dataset specifically designed to evaluate temporal understanding in the egocentric domain. EgoTempo emphasizes tasks that require integrating information across the entire video, ensuring that models would need to rely on temporal patterns rather than static cues or pre-existing knowledge. Extensive experiments on EgoTempo show that current MLLMs still fall short in temporal reasoning on egocentric videos, and thus we hope EgoTempo will catalyze new research in the field and inspire models that better capture the complexity of temporal dynamics. Dataset and code are available at https://github.com/google-research-datasets/egotempo.git.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Plizzari_Omnia_de_EgoTempo_Benchmarking_Temporal_Understanding_of_Multi-Modal_LLMs_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Plizzari_Omnia_de_EgoTempo_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Plizzari_2025_CVPR,
    author    = {Plizzari, Chiara and Tonioni, Alessio and Xian, Yongqin and Kulshrestha, Achin and Tombari, Federico},
    title     = {Omnia de EgoTempo: Benchmarking Temporal Understanding of Multi-Modal LLMs in Egocentric Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24129-24138}
}

raw_affiliation:
[{"affiliation": "Google", "city": "Mountain View", "country": "USA"}, {"affiliation": "Politecnico di Torino", "city": "Turin", "country": "Italy"}, {"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}]


title: ODHSR: Online Dense 3D Reconstruction of Humans and Scenes from Monocular Videos
authors: Zetong Zhang, Manuel Kaufmann, Lixin Xue, Jie Song, Martin R. Oswald
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21824-21835
affiliations: ["ETH Z\u00a8urich", "HKUST(GZ)", "HKUST", "University of Amsterdam"]
cities: ["Zurich", "Guangzhou", "Hong Kong", "Amsterdam"]
countries: ["Switzerland", "China", "China", "Netherlands"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_ODHSR_Online_Dense_3D_Reconstruction_of_Humans_and_Scenes_from_CVPR_2025_paper.html

abstract:
Creating a photorealistic scene and human reconstruction from a single monocular in-the-wild video figures prominently in the perception of a human-centric 3D world. Recent neural rendering advances have enabled holistic human-scene reconstruction but require pre-calibrated camera and human poses, and days of training time. In this work, we introduce a novel unified framework that simultaneously performs camera tracking, human pose estimation and human-scene reconstruction in an online fashion. 3D Gaussian Splatting is utilized to learn Gaussian primitives for humans and scenes efficiently, and reconstruction-based camera tracking and human pose estimation modules are designed to enable holistic understanding and effective disentanglement of pose and appearance. Specifically, we design a human deformation module to reconstruct the details and enhance generalizability to out-of-distribution poses faithfully. Aiming to learn the spatial correlation between human and scene accurately, we introduce occlusion-aware human silhouette rendering and monocular geometric priors, which further improve reconstruction quality. Experiments on the EMDB and NeuMan datasets demonstrate superior or on-par performance with existing methods in camera tracking, human pose estimation, novel view synthesis and runtime. Our project page is at https://eth-ait.github.io/ODHSR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_ODHSR_Online_Dense_3D_Reconstruction_of_Humans_and_Scenes_from_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_ODHSR_Online_Dense_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zetong and Kaufmann, Manuel and Xue, Lixin and Song, Jie and Oswald, Martin R.},
    title     = {ODHSR: Online Dense 3D Reconstruction of Humans and Scenes from Monocular Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21824-21835}
}

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


title: Identity-Preserving Text-to-Video Generation by Frequency Decomposition
authors: Shenghai Yuan, Jinfa Huang, Xianyi He, Yunyang Ge, Yujun Shi, Liuhan Chen, Jiebo Luo, Li Yuan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12978-12988
cities: ["Beijing", "Shenzhen", "Rochester", "Unknown", "Singapore"]
countries: ["China", "China", "USA", "Unknown", "Singapore"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yuan_Identity-Preserving_Text-to-Video_Generation_by_Frequency_Decomposition_CVPR_2025_paper.html

abstract:
Identity-preserving text-to-video (IPT2V) generation aims to create high-fidelity videos with consistent human identity.  It is an important task in video generation but remains an open problem for generative models. This paper pushes the technical frontier of IPT2V in two directions that have not been resolved in the literature: (1) A tuning-free pipeline without tedious case-by-case finetuning, and (2) A frequency-aware heuristic identity-preserving Diffusion Transformer (DiT)-based control scheme. To achieve these goals, we propose ConsisID, a tuning-free DiT-based controllable IPT2V model to keep human-identity consistent in the generated video. Inspired by prior findings in frequency analysis of vision/diffusion transformers, it employs identity-control signals base on frequency domain, since facial features can be decomposed into low-frequency global features (e.g., profile, proportions) and high-frequency intrinsic features (e.g., identity markers that remain unaffected by pose changes). Extensive experiments demonstrate that our frequency-aware heuristic scheme provides an optimal control solution for DiT-based models, making strides towards more effective IPT2V.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yuan_Identity-Preserving_Text-to-Video_Generation_by_Frequency_Decomposition_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yuan_Identity-Preserving_Text-to-Video_Generation_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yuan_2025_CVPR,
    author    = {Yuan, Shenghai and Huang, Jinfa and He, Xianyi and Ge, Yunyang and Shi, Yujun and Chen, Liuhan and Luo, Jiebo and Yuan, Li},
    title     = {Identity-Preserving Text-to-Video Generation by Frequency Decomposition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12978-12988}
}

raw_affiliation:
[{"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "University of Rochester", "city": "Rochester", "country": "USA"}, {"affiliation": "Rabbitpre Intelligence", "city": "Unknown", "country": "Unknown"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Peking University", "Peng Cheng Laboratory", "University of Rochester", "Rabbitpre Intelligence", "National University of Singapore"]


title: FreeGave: 3D Physics Learning from Dynamic Videos by Gaussian Velocity
authors: Jinxi Li, Ziyang Song, Siyuan Zhou, Bo Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12433-12443
affiliations: ["vLAR Group, The Hong Kong Polytechnic University"]
cities: ["Hong Kong"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_FreeGave_3D_Physics_Learning_from_Dynamic_Videos_by_Gaussian_Velocity_CVPR_2025_paper.html

abstract:
In this paper, we aim to model 3D scene geometry, appearance, and the underlying physics purely from multi-view videos. By applying various governing PDEs as PINN losses or incorporating physics simulation into neural networks, existing works often fail to learn complex physical motions at boundaries or require object priors such as masks or types. In this paper, we propose FreeGave to learn physics of complex dynamic 3D scenes without needing any object priors. The key to our approach is to introduce a physics code followed by a carefully designed divergence-free module for estimating a per-Gaussian velocity field, without relying on the inefficient PINN losses. Extensive experiments on three public datasets and a newly collected challenging real-world dataset demonstrate the superior performance of our method for future frame extrapolation and motion segmentation. Most notably, our investigation into the learned physics codes reveals that they truly learn meaningful 3D physical motion patterns in the absence of any human labels in training. Our code and data are available at https://github.com/vLAR-group/FreeGave.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_FreeGave_3D_Physics_Learning_from_Dynamic_Videos_by_Gaussian_Velocity_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_FreeGave_3D_Physics_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Jinxi and Song, Ziyang and Zhou, Siyuan and Yang, Bo},
    title     = {FreeGave: 3D Physics Learning from Dynamic Videos by Gaussian Velocity},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12433-12443}
}

raw_affiliation:
[{"affiliation": "vLAR Group, The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}]


title: SpiritSight Agent: Advanced GUI Agent with One Look
authors: Zhiyuan Huang, Ziming Cheng, Junting Pan, Zhaohui Hou, Mingjie Zhan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29490-29500
affiliations: ["SenseTime Research", "Beijing University of Posts and Telecommunications", "MMLab, CUHK"]
cities: ["Beijing", "Beijing", "Hong Kong"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_SpiritSight_Agent_Advanced_GUI_Agent_with_One_Look_CVPR_2025_paper.html

abstract:
Graphical User Interface (GUI) agents demonstrate promising potential in assisting human-computer interaction, automating human user's navigation on digital devices. An ideal GUI agent is expected to achieve high accuracy, low latency, and compatibility for different GUI platforms. Recent vision-based approaches have shown promise by leveraging advanced Vision Language Models (VLMs). While they generally meet the requirements of compatibility and low latency, these vision-based GUI agents tend to have low accuracy due to their limitations in element grounding. To address this issue, we propose SpiritSight, a vision-based, end-to-end GUI agent that excels in GUI navigation tasks across various GUI platforms. First, we create a multi-level, large-scale, high-quality GUI dataset called GUI-Lasagne using scalable methods, empowering SpiritSight with robust GUI understanding and grounding capabilities. Second, we introduce the Universal Block Parsing (UBP) method to resolve the ambiguity problem inherited from the dynamic resolution strategy, further enhancing SpiritSight's ability to ground GUI objects. Through these efforts, SpiritSight agent outperforms other advanced methods on diverse GUI benchmarks, demonstrating its superior capability and compatibility in GUI navigation tasks. The models and code will be made available upon publication.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_SpiritSight_Agent_Advanced_GUI_Agent_with_One_Look_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_SpiritSight_Agent_Advanced_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Zhiyuan and Cheng, Ziming and Pan, Junting and Hou, Zhaohui and Zhan, Mingjie},
    title     = {SpiritSight Agent: Advanced GUI Agent with One Look},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29490-29500}
}

raw_affiliation:
[{"affiliation": "SenseTime Research", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}, {"affiliation": "MMLab, CUHK", "city": "Hong Kong", "country": "China"}]


title: Zero-Shot Monocular Scene Flow Estimation in the Wild
authors: Yiqing Liang, Abhishek Badki, Hang Su, James Tompkin, Orazio Gallo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21031-21044
affiliations: ["NVIDIA", "Brown University"]
cities: ["Santa Clara", "Providence"]
countries: ["USA", "USA"]
oral: yes
award_candidate: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_Zero-Shot_Monocular_Scene_Flow_Estimation_in_the_Wild_CVPR_2025_paper.html

abstract:
Large models have shown generalization across datasets for many low-level vision tasks, like depth estimation, but no such general models exist for scene flow.Even though scene flow prediction has wide potential, its practical use is limited because of the lack of generalization of current predictive models. We identify three key challenges and propose solutions for each. First, we create a method that jointly estimates geometry and motion for accurate prediction. Second, we alleviate scene flow data scarcity with a data recipe that affords us 1M annotated training samples across diverse synthetic scenes. Third, we evaluate different parameterizations for scene flow prediction and adopt a natural and effective parameterization. Our model outperforms existing methods as well as baselines built on large-scale models in terms of 3D end-point error, and shows zero-shot generalization to the casually captured videos from DAVIS and the robotic manipulation scenes from RoboTAP. Overall, our approach makes scene flow prediction more practical in-the-wild.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_Zero-Shot_Monocular_Scene_Flow_Estimation_in_the_Wild_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_Zero-Shot_Monocular_Scene_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Yiqing and Badki, Abhishek and Su, Hang and Tompkin, James and Gallo, Orazio},
    title     = {Zero-Shot Monocular Scene Flow Estimation in the Wild},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21031-21044}
}

raw_affiliation:
[{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "Brown University", "city": "Providence", "country": "USA"}]


authors: Bizhu Wu, Jinheng Xie, Keming Shen, Zhe Kong, Jianfeng Ren, Ruibin Bai, Rong Qu, Linlin Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27849-27858
cities: ["Shenzhen", "Ningbo", "Shenzhen", "Singapore", "Shenzhen", "Nottingham"]
countries: ["China", "China", "China", "Singapore", "China", "United Kingdom"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_MG-MotionLLM_A_Unified_Framework_for_Motion_Comprehension_and_Generation_across_CVPR_2025_paper.html

title:
MG-MotionLLM: A Unified Framework for Motion Comprehension and Generation across Multiple Granularities

abstract:
Recent motion-aware large language models have demonstrated promising potential in unifying motion comprehension and generation. However, existing approaches primarily focus on coarse-grained motion-text modeling, where text describes the overall semantics of an entire motion sequence in just a few words. This limits their ability to handle fine-grained motion-relevant tasks, such as understanding and controlling the movements of specific body parts. To overcome this limitation, we pioneer MG-MotionLLM, a unified motion-language model for multi-granular motion comprehension and generation. We further introduce a comprehensive multi-granularity training scheme by incorporating a set of novel auxiliary tasks, such as localizing temporal boundaries of motion segments via detailed text as well as motion detailed captioning, to facilitate mutual reinforcement for motion-text modeling across various levels of granularity. Extensive experiments show that our MG-MotionLLM achieves superior performance on classical text-to-motion and motion-to-text tasks, and exhibits potential in novel fine-grained motion comprehension and editing tasks. Project page: CVI-SZU/MG-MotionLLM

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_MG-MotionLLM_A_Unified_Framework_for_Motion_Comprehension_and_Generation_across_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_MG-MotionLLM_A_Unified_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Bizhu and Xie, Jinheng and Shen, Keming and Kong, Zhe and Ren, Jianfeng and Bai, Ruibin and Qu, Rong and Shen, Linlin},
    title     = {MG-MotionLLM: A Unified Framework for Motion Comprehension and Generation across Multiple Granularities},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27849-27858}
}

raw_affiliation:
[{"affiliation": "Computer Vision Institute, School of Computer Science & Software Engineering, Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "School of Computer Science, University of Nottingham Ningbo China, Ningbo, China", "city": "Ningbo", "country": "China"}, {"affiliation": "Guangdong Provincial Key Laboratory of Intelligent Information Processing", "city": "Shenzhen", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Shenzhen Campus of Sun Yat-sen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "School of Computer Science, University of Nottingham, Nottingham, United Kingdom", "city": "Nottingham", "country": "United Kingdom"}]

affiliations:
["Computer Vision Institute, School of Computer Science & Software Engineering, Shenzhen University", "School of Computer Science, University of Nottingham Ningbo China, Ningbo, China", "Guangdong Provincial Key Laboratory of Intelligent Information Processing", "National University of Singapore", "Shenzhen Campus of Sun Yat-sen University", "School of Computer Science, University of Nottingham, Nottingham, United Kingdom"]


authors: Yuheng Feng, Changsong Wen, Zelin Peng, Li jiaye, Siyu Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24895-24904
affiliations: ["Fudan University", "Shanghai Jiao Tong University", "Shanghai Academy of AI for Science"]
cities: ["Shanghai", "Shanghai", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Feng_Retaining_Knowledge_and_Enhancing_Long-Text_Representations_in_CLIP_through_Dual-Teacher_CVPR_2025_paper.html

title:
Retaining Knowledge and Enhancing Long-Text Representations in CLIP through Dual-Teacher Distillation

abstract:
Contrastive language-image pretraining models such as CLIP have demonstrated remarkable performance in various text-image alignment tasks. However, the inherent 77-token input limitation and reliance on predominantly short-text training data restrict its ability to handle long-text tasks effectively. To overcome these constraints, we propose LongD-CLIP, a dual-teacher distillation framework designed to enhance long-text representation while mitigating knowledge forgetting. In our approach, a teacher model, fine-tuned on long-text data, distills rich representation knowledge into a student model, while the original CLIP serves as a secondary teacher to help the student retain its foundational knowledge. Extensive experiments reveal that LongD-CLIP significantly outperforms existing models across long-text retrieval, short-text retrieval, and zero-shot image classification tasks. For instance, in the image-to-text retrieval task on the ShareGPT4V test set, LongD-CLIP exceeds Long-CLIP's performance by 2.5%, achieving an accuracy of 98.3%. Similarly, on the Urban-1k dataset, it records a 9.2% improvement, reaching 91.9%, thereby underscoring its robust generalization capabilities. Additionally, the text encoder of LongD-CLIP exhibits reduced latent space drift and improved compatibility with existing generative models, effectively overcoming the 77-token input constraint.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_Retaining_Knowledge_and_Enhancing_Long-Text_Representations_in_CLIP_through_Dual-Teacher_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Feng_2025_CVPR,
    author    = {Feng, Yuheng and Wen, Changsong and Peng, Zelin and jiaye, Li and Zhu, Siyu},
    title     = {Retaining Knowledge and Enhancing Long-Text Representations in CLIP through Dual-Teacher Distillation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24895-24904}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Academy of AI for Science", "city": "Shanghai", "country": "China"}]


title: MMRL: Multi-Modal Representation Learning for Vision-Language Models
authors: Yuncheng Guo, Xiaodong Gu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25015-25025
affiliations: ["Department of Electronic Engineering, Fudan University"]
cities: ["Shanghai"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_MMRL_Multi-Modal_Representation_Learning_for_Vision-Language_Models_CVPR_2025_paper.html

abstract:
Large-scale pre-trained Vision-Language Models (VLMs) have become essential for transfer learning across diverse tasks. However, adapting these models with limited few-shot data often leads to overfitting, diminishing their performance on new tasks. To tackle this issue, we propose a novel Multi-Modal Representation Learning (MMRL) framework that introduces a shared, learnable, and modality-agnostic representation space. MMRL projects the space tokens to text and image representation tokens, facilitating more effective multi-modal interactions. Unlike previous approaches that solely optimize class token features, MMRL integrates representation tokens at higher layers of the encoders--where dataset-specific features are more prominent--while preserving generalized knowledge in the lower layers. During training, both representation and class features are optimized, with trainable projection layer applied to the representation tokens, whereas the class token projection layer remains frozen to retain pre-trained knowledge. Furthermore, a regularization term is introduced to align the class features and text features with the zero-shot features from the frozen VLM, thereby safeguarding the model's generalization capacity. For inference, a decoupling strategy is employed, wherein both representation and class features are utilized for base classes, while only the class features, which retain more generalized knowledge, are used for new tasks. Extensive experiments across 15 datasets demonstrate that MMRL outperforms state-of-the-art methods, achieving a balanced trade-off between task-specific adaptation and generalization. Code is available at https://github.com/yunncheng/MMRL.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_MMRL_Multi-Modal_Representation_Learning_for_Vision-Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_MMRL_Multi-Modal_Representation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Yuncheng and Gu, Xiaodong},
    title     = {MMRL: Multi-Modal Representation Learning for Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25015-25025}
}

raw_affiliation:
[{"affiliation": "Department of Electronic Engineering, Fudan University", "city": "Shanghai", "country": "China"}]


title: Optical-Flow Guided Prompt Optimization for Coherent Video Generation
authors: Hyelin Nam, Jaemin Kim, Dohun Lee, Jong Chul Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7837-7846
affiliations: ["Kim Jaechul Graduate School of AI, KAIST", "EverEx"]
cities: ["Daejeon", "Unknown"]
countries: ["South Korea", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nam_Optical-Flow_Guided_Prompt_Optimization_for_Coherent_Video_Generation_CVPR_2025_paper.html

abstract:
While text-to-video diffusion models have made significant strides, many still face challenges in generating videos with temporal consistency. Within diffusion frameworks, guidance techniques have proven effective in enhancing output quality during inference; however, applying these methods to video diffusion models introduces additional complexity of handling computations across entire sequences. To address this, we propose a novel framework called MotionPrompt that guides the video generation process via optical flow. Specifically, we train a discriminator to distinguish optical flow between random pairs of frames from real videos and generated ones. Given that prompts can influence the entire video, we optimize learnable token embeddings during reverse sampling steps by using gradients from a trained discriminator applied to random frame pairs. This approach allows our method to generate visually coherent video sequences that closely reflect natural motion dynamics, without compromising the fidelity of the generated content. We demonstrate the effectiveness of our approach across various models. Project Page: https://motionprompt.github.io

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nam_Optical-Flow_Guided_Prompt_Optimization_for_Coherent_Video_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nam_Optical-Flow_Guided_Prompt_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nam_2025_CVPR,
    author    = {Nam, Hyelin and Kim, Jaemin and Lee, Dohun and Ye, Jong Chul},
    title     = {Optical-Flow Guided Prompt Optimization for Coherent Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7837-7846}
}

raw_affiliation:
[{"affiliation": "Kim Jaechul Graduate School of AI, KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "EverEx", "city": "Unknown", "country": "Unknown"}]


title: MOS: Modeling Object-Scene Associations in Generalized Category Discovery
authors: Zhengyuan Peng, Jinpeng Ma, Zhimin Sun, Ran Yi, Haichuan Song, Xin Tan, Lizhuang Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15118-15128
affiliations: ["Shanghai Jiao Tong University", "East China Normal University", "Chongqing University"]
cities: ["Shanghai", "Shanghai", "Chongqing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Peng_MOS_Modeling_Object-Scene_Associations_in_Generalized_Category_Discovery_CVPR_2025_paper.html

abstract:
Generalized Category Discovery (GCD) is a classification task that aims to classify both base and novel classes in unlabeled images, using knowledge from a labeled dataset. In GCD, previous research overlooks scene information or treats it as noise, reducing its impact during model training. However, in this paper, we argue that scene information should be viewed as a strong prior for inferring novel classes. We attribute the misinterpretation of scene information to a key factor: the Ambiguity Challenge inherent in GCD. Specifically, novel objects in base scenes might be wrongly classified into base categories, while base objects in novel scenes might be mistakenly recognized as novel categories. Once the ambiguity challenge is addressed, scene information can reach its full potential, significantly enhancing the performance of GCD models. To more effectively leverage scene information, we propose the Modeling Object-Scene Associations (MOS) framework, which utilizes a simple MLP-based scene-awareness module to enhance GCD performance. It achieves an exceptional average accuracy improvement of 4% on the challenging fine-grained datasets compared to state-of-the-art methods, emphasizing its superior performance in fine-grained GCD. The code is publicly available at https://github.com/JethroPeng/MOS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Peng_MOS_Modeling_Object-Scene_Associations_in_Generalized_Category_Discovery_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Peng_MOS_Modeling_Object-Scene_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Peng_2025_CVPR,
    author    = {Peng, Zhengyuan and Ma, Jinpeng and Sun, Zhimin and Yi, Ran and Song, Haichuan and Tan, Xin and Ma, Lizhuang},
    title     = {MOS: Modeling Object-Scene Associations in Generalized Category Discovery},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15118-15128}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "East China Normal University", "city": "Shanghai", "country": "China"}, {"affiliation": "Chongqing University", "city": "Chongqing", "country": "China"}]


authors: Jiawei Tan, Hongxing Wang, Junwu Weng, Jiaxin Li, Zhilong Ou, Kang Dang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24180-24189
cities: ["Chongqing", "Chongqing", "Unknown", "Suzhou"]
countries: ["China", "China", "Unknown", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tan_Anchor-Aware_Similarity_Cohesion_in_Target_Frames_Enables_Predicting_Temporal_Moment_CVPR_2025_paper.html

title:
Anchor-Aware Similarity Cohesion in Target Frames Enables Predicting Temporal Moment Boundaries in 2D

abstract:
Video moment retrieval aims to locate specific moments from a video according to the query text. This task presents two main challenges: i) aligning the query and video frames at the feature level, and ii) projecting the query-aligned frame features to the start and end boundaries of the matching interval. Previous work commonly involves all frames in feature alignment, easy to cause aligning irrelevant frames with the query. Furthermore, they forcibly map visual features to interval boundaries but ignoring the information gap between them, yielding suboptimal performance. In this study, to reduce distraction from irrelevant frames, we designate an anchor frame as that with the maximum query-frame relevance measured by the established Vision-Language Model. Via similarity comparison between the anchor frame and the others, we produce a semantically compact segment around the anchor frame, which serves as a guide to align features of query and related frames. We observe that such a feature alignment will make similarity cohesive between target frames, which enables us to predict the interval boundaries by a single point detection in the 2D semantic similarity space of frames, thus well bridging the information gap between frame semantics and temporal boundaries. Experimental results across various datasets demonstrate that our approach significantly improves the alignment between queries and video frames while effectively predicting temporal moment boundaries. Especially, on QVHighlights Test and ActivityNet Captions datasets, our proposed approach achieves 3.8% and 7.4% respectively higher than current state-of-the-art R1@.7 performance. The code is available at https://github.com/ExMorgan-Alter/AFAFSGD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tan_Anchor-Aware_Similarity_Cohesion_in_Target_Frames_Enables_Predicting_Temporal_Moment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tan_Anchor-Aware_Similarity_Cohesion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tan_2025_CVPR,
    author    = {Tan, Jiawei and Wang, Hongxing and Weng, Junwu and Li, Jiaxin and Ou, Zhilong and Dang, Kang},
    title     = {Anchor-Aware Similarity Cohesion in Target Frames Enables Predicting Temporal Moment Boundaries in 2D},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24180-24189}
}

raw_affiliation:
[{"affiliation": "School of Big Data and Software Engineering, Chongqing University, China", "city": "Chongqing", "country": "China"}, {"affiliation": "Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education, China", "city": "Chongqing", "country": "China"}, {"affiliation": "ByteDance Intelligent Creation", "city": "Unknown", "country": "Unknown"}, {"affiliation": "School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi\u2019an Jiaotong-Liverpool University, Suzhou, China", "city": "Suzhou", "country": "China"}]

affiliations:
["School of Big Data and Software Engineering, Chongqing University, China", "Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education, China", "ByteDance Intelligent Creation", "School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi\u2019an Jiaotong-Liverpool University, Suzhou, China"]


title: Test-time Augmentation Improves Efficiency in Conformal Prediction
authors: Divya Shanmugam, Helen Lu, Swami Sankaranarayanan, John Guttag
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20622-20631
affiliations: ["Massachusetts Institute of Technology, CSAIL"]
cities: ["Cambridge"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shanmugam_Test-time_Augmentation_Improves_Efficiency_in_Conformal_Prediction_CVPR_2025_paper.html

abstract:
A conformal classifier produces a set of predicted classes and provides a probabilistic guarantee that the set includes the true class. Unfortunately, it is often the case that conformal classifiers produce uninformatively large sets. In this work, we show that test-time augmentation (TTA)---a technique that introduces inductive biases during inference---reduces the size of the sets produced by conformal classifiers. Our approach is flexible, computationally efficient, and effective. It can be combined with any conformal score, requires no model retraining, and reduces prediction set sizes by 10%-14% on average. We conduct an evaluation of the approach spanning three datasets, three models, two established conformal scoring methods, different guarantee strengths, and several distribution shifts to show when and why test-time augmentation is a useful addition to the conformal pipeline.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shanmugam_Test-time_Augmentation_Improves_Efficiency_in_Conformal_Prediction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shanmugam_Test-time_Augmentation_Improves_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shanmugam_2025_CVPR,
    author    = {Shanmugam, Divya and Lu, Helen and Sankaranarayanan, Swami and Guttag, John},
    title     = {Test-time Augmentation Improves Efficiency in Conformal Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20622-20631}
}

raw_affiliation:
[{"affiliation": "Massachusetts Institute of Technology, CSAIL", "city": "Cambridge", "country": "USA"}]


title: Breaking the Low-Rank Dilemma of Linear Attention
authors: Qihang Fan, Huaibo Huang, Ran He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25271-25280
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fan_Breaking_the_Low-Rank_Dilemma_of_Linear_Attention_CVPR_2025_paper.html

abstract:
The Softmax attention mechanism in Transformer models is notoriously computationally expensive, particularly due to its quadratic complexity, posing significant challenges in vision applications. In contrast, linear attention provides a far more efficient solution by reducing the complexity to linear levels. However, compared to Softmax attention, linear attention often experiences significant performance degradation. Our experiments indicate that this performance drop is due to the low-rank nature of linear attention's feature map, which hinders its ability to adequately model complex spatial information. In this paper, to break the low-rank dilemma of linear attention, we conduct rank analysis from two perspectives: the kv buffer and the output features. Consequently, we introduce **Rank-Augmented Linear Attention** (RALA), which rivals the performance of Softmax attention while maintaining linear complexity and high efficiency. Based on RALA, we construct the **Rank-Augmented Vision Linear Transformer** (RAVLT). Extensive experiments demonstrate that RAVLT achieves excellent performance across various vision tasks. Specifically, without using any additional labels, data, or supervision during training, RAVLT achieves an **84.4%** Top-1 accuracy on ImageNet-1k with only **26M** parameters and **4.6G** FLOPs. This result significantly surpasses previous linear attention mechanisms, fully illustrating the potential of RALA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fan_Breaking_the_Low-Rank_Dilemma_of_Linear_Attention_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fan_Breaking_the_Low-Rank_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fan_2025_CVPR,
    author    = {Fan, Qihang and Huang, Huaibo and He, Ran},
    title     = {Breaking the Low-Rank Dilemma of Linear Attention},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25271-25280}
}

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

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


title: StoryGPT-V: Large Language Models as Consistent Story Visualizers
authors: Xiaoqian Shen, Mohamed Elhoseiny
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13273-13283
affiliations: ["KAUST", "KAUST"]
cities: ["Thuwal", "Thuwal"]
countries: ["Saudi Arabia", "Saudi Arabia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shen_StoryGPT-V_Large_Language_Models_as_Consistent_Story_Visualizers_CVPR_2025_paper.html

abstract:
Recent generative models have demonstrated impressive capabilities in generating realistic and visually pleasing images grounded on textual prompts. Nevertheless, a significant challenge remains in applying these models for the more intricate task of story visualization. Since it requires resolving pronouns (he, she, they) in the frame descriptions, i.e., anaphora resolution, and ensuring consistent characters and background synthesis across frames. Yet, the emerging Large Language Model (LLM) showcases robust reasoning abilities to navigate through ambiguous references and process extensive sequences. Therefore, we introduce StoryGPT-V, which leverages the merits of the latent diffusion (LDM) and LLM to produce images with consistent and high-quality characters grounded on given story descriptions. First, we train a character-aware LDM, which takes character-augmented semantic embedding as input and includes the supervision of the cross-attention map using character segmentation masks, aiming to enhance character generation accuracy and faithfulness. In the second stage, we enable an alignment between the output of LLM and the character-augmented embedding residing in the input space of the first-stage model. This harnesses the reasoning ability of LLM to address ambiguous references and the comprehension capability to memorize the context. We conduct comprehensive experiments on two visual story visualization benchmarks. Our model reports superior quantitative results and consistently generates accurate characters of remarkable quality with low memory consumption. Our code is publicly available at: https://xiaoqian-shen.github.io/StoryGPT-V.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_StoryGPT-V_Large_Language_Models_as_Consistent_Story_Visualizers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shen_StoryGPT-V_Large_Language_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shen_2025_CVPR,
    author    = {Shen, Xiaoqian and Elhoseiny, Mohamed},
    title     = {StoryGPT-V: Large Language Models as Consistent Story Visualizers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13273-13283}
}

raw_affiliation:
[{"affiliation": "KAUST", "city": "Thuwal", "country": "Saudi Arabia"}, {"affiliation": "KAUST", "city": "Thuwal", "country": "Saudi Arabia"}]


authors: Enshen Zhou, Qi Su, Cheng Chi, Zhizheng Zhang, Zhongyuan Wang, Tiejun Huang, Lu Sheng, He Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6919-6929
affiliations: ["Beihang University", "Peking University", "Beijing Academy of Artificial Intelligence", "Galbot"]
cities: ["Beijing", "Beijing", "Beijing", "Unknown"]
countries: ["China", "China", "China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Code-as-Monitor_Constraint-aware_Visual_Programming_for_Reactive_and_Proactive_Robotic_Failure_CVPR_2025_paper.html

title:
Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection

abstract:
Automatic detection and prevention of open-set failures are crucial in closed-loop robotic systems. Recent studies often struggle to simultaneously identify unexpected failures reactively after they occur and prevent foreseeable ones proactively. To this end, we propose Code-as-Monitor (CaM), a novel paradigm leveraging the vision-language model (VLM) for both open-set reactive and proactive failure detection. The core of our method is to formulate both tasks as a unified set of spatio-temporal constraint satisfaction problems and use VLM-generated code to evaluate them for real-time monitoring. To enhance the accuracy and efficiency of monitoring, we further introduce constraint elements that abstract constraint-related entities or their parts into compact geometric elements. This approach offers greater generality, simplifies tracking, and facilitates constraint-aware visual programming by leveraging these elements as visual prompts. Experiments show that CaM achieves a 28.7% higher success rate and reduces execution time by 31.8% under severe disturbances compared to baselines across three simulators and a real-world setting. Moreover, CaM can be integrated with open-loop control policies to form closed-loop systems, enabling long-horizon tasks in cluttered scenes with dynamic environments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Code-as-Monitor_Constraint-aware_Visual_Programming_for_Reactive_and_Proactive_Robotic_Failure_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Code-as-Monitor_Constraint-aware_Visual_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Enshen and Su, Qi and Chi, Cheng and Zhang, Zhizheng and Wang, Zhongyuan and Huang, Tiejun and Sheng, Lu and Wang, He},
    title     = {Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6919-6929}
}

raw_affiliation:
[{"affiliation": "Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Academy of Artificial Intelligence", "city": "Beijing", "country": "China"}, {"affiliation": "Galbot", "city": "Unknown", "country": "Unknown"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25156-25165
cities: ["Shenzhen", "Shenzhen", "Shenzhen", "Hong Kong", "Shenzhen", "Beijing"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Embracing_Collaboration_Over_Competition_Condensing_Multiple_Prompts_for_Visual_In-Context_CVPR_2025_paper.html

title:
Embracing Collaboration Over Competition: Condensing Multiple Prompts for Visual In-Context Learning

authors:
Jinpeng Wang, Tianci Luo, Yaohua Zha, Yan Feng, Ruisheng Luo, Bin Chen, Tao Dai, Long Chen, Yaowei Wang, Shu-Tao Xia

abstract:
Visual In-Context Learning (VICL) enables adaptively solving vision tasks by leveraging pixel demonstrations, mimicking human-like task completion through analogy. Prompt selection is critical in VICL, but current methods assume the existence of a single "ideal" prompt in a pool of candidates, which in practice may not hold true. Multiple suitable prompts may exist, but individually they often fall short, leading to difficulties in selection and the exclusion of useful context. To address this, we propose a new perspective: prompt condensation. Rather than relying on a single prompt, candidate prompts collaborate to efficiently integrate informative contexts without sacrificing resolution. We devise Condenser, a lightweight external plugin that compresses relevant fine-grained context across multiple prompts. Optimized end-to-end with the backbone, Condenser ensures accurate integration of contextual cues. Experiments demonstrate Condenser outperforms state-of-the-arts across benchmark tasks, showing superior context compression, scalability with more prompts, and enhanced computational efficiency compared to ensemble methods, positioning it as a highly competitive solution for VICL. Code is open-sourced at https://github.com/gimpong/CVPR25-Condenser.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Embracing_Collaboration_Over_Competition_Condensing_Multiple_Prompts_for_Visual_In-Context_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Jinpeng and Luo, Tianci and Zha, Yaohua and Feng, Yan and Luo, Ruisheng and Chen, Bin and Dai, Tao and Chen, Long and Wang, Yaowei and Xia, Shu-Tao},
    title     = {Embracing Collaboration Over Competition: Condensing Multiple Prompts for Visual In-Context Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25156-25165}
}

raw_affiliation:
[{"affiliation": "Tsinghua Shenzhen International Graduate School, Tsinghua University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Harbin Institute of Technology, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "Research Center of Artificial Intelligence, Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Meituan", "city": "Beijing", "country": "China"}]

affiliations:
["Tsinghua Shenzhen International Graduate School, Tsinghua University", "Harbin Institute of Technology, Shenzhen", "Shenzhen University", "The Hong Kong University of Science and Technology", "Research Center of Artificial Intelligence, Peng Cheng Laboratory", "Meituan"]


authors: Tengyu Ma, Long Ma, Ziye Li, Yuetong Wang, Jinyuan Liu, Chengpei Xu, Risheng Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2323-2332
affiliations: ["Dalian University of Technology", "University of New South Wales"]
cities: ["Dalian", "Sydney"]
countries: ["China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Rethinking_Reconstruction_and_Denoising_in_the_Dark_New_Perspective_General_CVPR_2025_paper.html

title:
Rethinking Reconstruction and Denoising in the Dark: New Perspective, General Architecture and Beyond

abstract:
Recently, enhancing image quality in the original RAW domain has garnered significant attention, with denoising and reconstruction emerging as fundamental tasks. Although some works attempt to couple these tasks, they primarily focus on cascade learning while neglecting task associativity within a broader parameter space, leading to suboptimal performance. This work introduces a novel approach by rethinking denoising and reconstruction from a "backbone-head" perspective, leveraging the stronger shared parameter space offered by the backbone, compared to the encoder used in existing works. We derive task-specific heads with fewer parameters to mitigate learning pressure. By incorporating chromaticity-and-noise perception module into the backbone and introducing task-specific supervision during training, we enable simultaneous high-quality results for reconstruction and denoising. Additionally, we design a dual-head interaction module to capture the latent correspondence between the two tasks, significantly enhancing multi-task accuracy. Extensive experiments validate the superiority of the proposed method. Code is available at: https://github.com/csmty/CANS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_Rethinking_Reconstruction_and_Denoising_in_the_Dark_New_Perspective_General_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Tengyu and Ma, Long and Li, Ziye and Wang, Yuetong and Liu, Jinyuan and Xu, Chengpei and Liu, Risheng},
    title     = {Rethinking Reconstruction and Denoising in the Dark: New Perspective, General Architecture and Beyond},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2323-2332}
}

raw_affiliation:
[{"affiliation": "Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "University of New South Wales", "city": "Sydney", "country": "Australia"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12789-12798
affiliations: ["Samsung AI Center", "Queen Mary University of London"]
cities: ["Cambridge", "London"]
countries: ["UK", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hadji_Edge-SD-SR_Low_Latency_and_Parameter_Efficient_On-device_Super-Resolution_with_Stable_CVPR_2025_paper.html

title:
Edge-SD-SR: Low Latency and Parameter Efficient On-device Super-Resolution with Stable Diffusion via Bidirectional Conditioning

authors:
Isma Hadji, Mehdi Noroozi, Victor Escorcia, Anestis Zaganidis, Brais Martinez, Georgios Tzimiropoulos

abstract:
There has been immense progress recently in the visual quality of Stable Diffusion-based Super Resolution (SD-SR). However, deploying large diffusion models on computationally restricted devices such as mobile phones remains impractical due to the large model size and high latency. This is compounded for SR as it often operates at high res (e.g. 4Kx3K). In this work, we introduce Edge-SD-SR, the first parameter efficient and low latency diffusion model for image super-resolution. Edge-SD-SR consists of   169M parameters, including UNet, encoder and decoder, and has a complexity of only   142 GFLOPs. To maintain a high visual quality on such low compute budget, we introduce a number of training strategies: (i) A novel conditioning mechanism on the low-resolution input, coined bidirectional conditioning, which tailors the SD model for the SR task. (ii) Joint training of the UNet and encoder, while decoupling the encodings of the HR and LR images and using a dedicated schedule. (iii) Finetuning the decoder using the UNet's output to directly tailor the decoder to the latents obtained at inference time. Edge-SD-SR runs efficiently on device, e.g. it can upscale a 128x128 patch to 512x512 in 38 msec while running on a Samsung S24 DSP, and of a 512 x 512 to 2, 048 x 2, 048 (requiring 25 model evaluations) in just   1.1 sec. Furthermore, we show that Edge-SD-SR matches or even outperforms state-of-the-art SR approaches on the most established SR benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hadji_Edge-SD-SR_Low_Latency_and_Parameter_Efficient_On-device_Super-Resolution_with_Stable_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Hadji_2025_CVPR,
    author    = {Hadji, Isma and Noroozi, Mehdi and Escorcia, Victor and Zaganidis, Anestis and Martinez, Brais and Tzimiropoulos, Georgios},
    title     = {Edge-SD-SR: Low Latency and Parameter Efficient On-device Super-Resolution with Stable Diffusion via Bidirectional Conditioning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12789-12798}
}

raw_affiliation:
[{"affiliation": "Samsung AI Center", "city": "Cambridge", "country": "UK"}, {"affiliation": "Queen Mary University of London", "city": "London", "country": "UK"}]


title: Unity in Diversity: Video Editing via Gradient-Latent Purification
authors: Junyu Gao, Kunlin Yang, Xuan Yao, Yufan Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23401-23411
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_Unity_in_Diversity_Video_Editing_via_Gradient-Latent_Purification_CVPR_2025_paper.html

abstract:
Recently, text-driven video editing methods that optimize target latent representations have garnered significant attention and demonstrated promising results. However, these methods rely on self-supervised objectives to compute the gradients needed for updating latent representations, which inevitably introduces gradient noise, compromising content generation quality. Additionally, it is challenging to determine the optimal stopping point for the editing process, making it difficult to achieve an optimal solution for the latent representation. To address these issues, we propose a unified gradient-latent purification framework that collects gradient and latent information across different stages to identify effective and concordant update directions. We design a local coordinate system construction method based on feature decomposition, enabling short-term gradients and final-stage latents to be reprojected onto new axes. Then, we employ tailored coefficient regularization terms to effectively aggregate the decomposed information. Additionally, a temporal smoothing axis extension strategy is developed to enhance the temporal coherence of the generated content. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art methods across various editing tasks, delivering superior editing performance. Project page is available in https://unity-in-diversity-editing.github.io.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_Unity_in_Diversity_Video_Editing_via_Gradient-Latent_Purification_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Junyu and Yang, Kunlin and Yao, Xuan and Hu, Yufan},
    title     = {Unity in Diversity: Video Editing via Gradient-Latent Purification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23401-23411}
}

raw_affiliation:
[{"affiliation": "School of Intelligence Science and Technology, University of Science and Technology Beijing", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of Multimodal Arti\ufb01cial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences (CASIA)", "city": "Beijing", "country": "China"}]

affiliations:
["School of Intelligence Science and Technology, University of Science and Technology Beijing", "State Key Laboratory of Multimodal Arti\ufb01cial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences (CASIA)"]


authors: Hongda Liu, Yunfan Liu, Min Ren, Hao Wang, Yunlong Wang, Zhenan Sun
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29248-29257
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Revealing_Key_Details_to_See_Differences_A_Novel_Prototypical_Perspective_CVPR_2025_paper.html

title:
Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action Recognition

abstract:
In skeleton-based action recognition, a key challenge is distinguishing between actions with similar trajectories of joints due to the lack of image-level details in skeletal representations. Recognizing that the differentiation of similar actions relies on subtle motion details in specific body parts, we direct our approach to focus on the fine-grained motion of local skeleton components. To this end, we introduce ProtoGCN, a Graph Convolutional Network (GCN)-based model that breaks down the dynamics of entire skeleton sequences into a combination of learnable prototypes representing core motion patterns of action units. By contrasting the reconstruction of prototypes, ProtoGCN can effectively identify and enhance the discriminative representation of similar actions. Without bells and whistles, ProtoGCN achieves state-of-the-art performance on multiple benchmark datasets, including NTU RGB+D, NTU RGB+D 120, Kinetics-Skeleton, and FineGYM, which demonstrates the effectiveness of the proposed method. The code is available at https://github.com/firework8/ProtoGCN.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Revealing_Key_Details_to_See_Differences_A_Novel_Prototypical_Perspective_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Revealing_Key_Details_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Hongda and Liu, Yunfan and Ren, Min and Wang, Hao and Wang, Yunlong and Sun, Zhenan},
    title     = {Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29248-29257}
}

raw_affiliation:
[{"affiliation": "NLPR, Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Normal University", "city": "Beijing", "country": "China"}]

affiliations:
["NLPR, Institute of Automation, Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "Beijing Normal University"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25842-25853
affiliations: ["1", "2,3", "4"]
cities: ["Haifa", "Munich", "Haifa"]
countries: ["Israel", "Germany", "Israel"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dages_Finsler_Multi-Dimensional_Scaling_Manifold_Learning_for_Asymmetric_Dimensionality_Reduction_and_CVPR_2025_paper.html

title:
Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding

authors:
Thomas Dagès, Simon Weber, Ya-Wei Eileen Lin, Ronen Talmon, Daniel Cremers, Michael Lindenbaum, Alfred M. Bruckstein, Ron Kimmel

abstract:
Dimensionality reduction is a fundamental task that aims to simplify complex data by reducing its feature dimensionality while preserving essential patterns, with core applications in data analysis and visualisation. To preserve the underlying data structure, multi-dimensional scaling (MDS) methods focus on preserving pairwise dissimilarities, such as distances. They optimise the embedding to have pairwise distances as close as possible to the data dissimilarities. However, the current standard is limited to embedding data in Riemannian manifolds. Motivated by the lack of asymmetry in the Riemannian metric of the embedding space, this paper extends the MDS problem to a natural asymmetric generalisation of Riemannian manifolds called Finsler manifolds. Inspired by Euclidean space, we define a canonical Finsler space for embedding asymmetric data. Due to its simplicity with respect to geodesics, data representation in this space is both intuitive and simple to analyse. We demonstrate that our generalisation benefits from the same theoretical convergence guarantees. We reveal the effectiveness of our Finsler embedding across various types of non-symmetric data, highlighting its value in applications such as data visualisation, dimensionality reduction, directed graph embedding, and link prediction.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dages_Finsler_Multi-Dimensional_Scaling_Manifold_Learning_for_Asymmetric_Dimensionality_Reduction_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dages_Finsler_Multi-Dimensional_Scaling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dages_2025_CVPR,
    author    = {Dag\`es, Thomas and Weber, Simon and Lin, Ya-Wei Eileen and Talmon, Ronen and Cremers, Daniel and Lindenbaum, Michael and Bruckstein, Alfred M. and Kimmel, Ron},
    title     = {Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25842-25853}
}

raw_affiliation:
[{"affiliation": "1", "city": "Haifa", "country": "Israel"}, {"affiliation": "2,3", "city": "Munich", "country": "Germany"}, {"affiliation": "4", "city": "Haifa", "country": "Israel"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26181-26191
cities: ["Beijing", "Hong Kong", "Shanghai", "Changsha", "Shenzhen", "Hong Kong"]
countries: ["China", "China", "China", "China", "China", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Han_VideoEspresso_A_Large-Scale_Chain-of-Thought_Dataset_for_Fine-Grained_Video_Reasoning_via_CVPR_2025_paper.html

title:
VideoEspresso: A Large-Scale Chain-of-Thought Dataset for Fine-Grained Video Reasoning via Core Frame Selection

authors:
Songhao Han, Wei Huang, Hairong Shi, Le Zhuo, Xiu Su, Shifeng Zhang, Xu Zhou, Xiaojuan Qi, Yue Liao, Si Liu

abstract:
The advancement of Large Vision Language Models (LVLMs) has significantly improved multimodal understanding, yet challenges remain in video reasoning tasks due to the scarcity of high-quality, large-scale datasets. Existing video question-answering (VideoQA) datasets often rely on costly manual annotations with insufficient granularity or automatic construction methods with redundant frame-by-frame analysis, limiting their scalability and effectiveness for complex reasoning. To address these challenges, we introduce VideoEspresso, a novel dataset that features VideoQA pairs preserving essential spatial details and temporal coherence, along with multimodal annotations of intermediate reasoning steps. Our construction pipeline employs a semantic-aware method to reduce redundancy, followed by generating QA pairs using GPT-4o. We further develop video Chain-of-Thought (CoT) annotations to enrich reasoning processes, guiding GPT-4o in extracting logical relationships from QA pairs and video content. To exploit the potential of high-quality VideoQA pairs, we propose a Hybrid LVLMs Collaboration framework, featuring a Frame Selector and a two-stage instruction fine-tuned reasoning LVLM. This framework adaptively selects core frames and performs CoT reasoning using multimodal evidence. Evaluated on our proposed benchmark with 14 tasks against 9 popular LVLMs, our method outperforms existing baselines on most tasks, demonstrating superior video reasoning capabilities. Our code and dataset have been released at: https://github.com/hshjerry/VideoEspresso

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Han_VideoEspresso_A_Large-Scale_Chain-of-Thought_Dataset_for_Fine-Grained_Video_Reasoning_via_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Han_2025_CVPR,
    author    = {Han, Songhao and Huang, Wei and Shi, Hairong and Zhuo, Le and Su, Xiu and Zhang, Shifeng and Zhou, Xu and Qi, Xiaojuan and Liao, Yue and Liu, Si},
    title     = {VideoEspresso: A Large-Scale Chain-of-Thought Dataset for Fine-Grained Video Reasoning via Core Frame Selection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26181-26191}
}

raw_affiliation:
[{"affiliation": "Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shanghai AI Lab", "city": "Shanghai", "country": "China"}, {"affiliation": "Central South University", "city": "Changsha", "country": "China"}, {"affiliation": "Sangfor Technologies Inc.", "city": "Shenzhen", "country": "China"}, {"affiliation": "CUHK", "city": "Hong Kong", "country": "China"}]

affiliations:
["Beihang University", "University of Hong Kong", "Shanghai AI Lab", "Central South University", "Sangfor Technologies Inc.", "CUHK"]


title: INFP: Audio-Driven Interactive Head Generation in Dyadic Conversations
authors: Yongming Zhu, Longhao Zhang, Zhengkun Rong, Tianshu Hu, Shuang Liang, Zhipeng Ge
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10667-10677
raw_affiliation: [{"affiliation": "Bytedance", "city": "Beijing", "country": "China"}]
affiliations: ["Bytedance"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_INFP_Audio-Driven_Interactive_Head_Generation_in_Dyadic_Conversations_CVPR_2025_paper.html

abstract:
Imagine having a conversation with a socially intelligent agent. It can attentively listen to your words and offer visual and linguistic feedback promptly. This seamless interaction allows for multiple rounds of conversation to flow smoothly and naturally. In pursuit of actualizing it, we propose INFP, a novel audio-driven head generation framework for dyadic interaction. Unlike previous head generation works that only focus on single-sided communication, or require manual role assignment and explicit role switching, our model drives the agent portrait dynamically alternates between speaking and listening state, guided by the input dyadic audio. Specifically, INFP comprises a Motion-Based Head Imitation stage and an Audio-Guided Motion Generation stage. The first stage learns to project facial communicative behaviors from real-life conversation videos into a low-dimensional motion latent space, and use the motion latent codes to animate a static image. The second stage learns the mapping from the input dyadic audio to motion latent codes through denoising, leading to the audio-driven head generation in interactive scenarios. To facilitate this line of research, we introduce DyConv, a large scale dataset of rich dyadic conversations collected from the Internet. Extensive experiments and visualizations demonstrate superior performance and effectiveness of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_INFP_Audio-Driven_Interactive_Head_Generation_in_Dyadic_Conversations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_INFP_Audio-Driven_Interactive_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Yongming and Zhang, Longhao and Rong, Zhengkun and Hu, Tianshu and Liang, Shuang and Ge, Zhipeng},
    title     = {INFP: Audio-Driven Interactive Head Generation in Dyadic Conversations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10667-10677}
}


title: Federated Learning with Domain Shift Eraser
authors: Zheng Wang, Zihui Wang, Zheng Wang, Xiaoliang Fan, Cheng Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4978-4987
cities: ["Xiamen", "Xiamen", "Shanghai", "Xiamen", "Shenzhen"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Federated_Learning_with_Domain_Shift_Eraser_CVPR_2025_paper.html

abstract:
Federated learning (FL) is emerging as a promising technique for collaborative learning without local data leaving their devices. However, clients' data originating from diverse domains may degrade model performance due to domain shifts, preventing the model from learning consistent representation space. In this paper, we propose a novel FL framework, Federated Domain Shift Eraser (FDSE), to improve model performance by differently erasing each client's domain skew and enhancing their consensus. First, we formulate the model forward passing as an iterative deskewing process that extracts and then deskews features alternatively. This is efficiently achieved by decomposing each original layer in the neural network into a Domain-agnostic Feature Extractor (DFE) and a Domain-specific Skew Eraser (DSE). Then, a regularization term is applied to promise the effectiveness of feature deskewing by pulling local statistics of DSE's outputs close to the globally consistent ones. Finally, DFE modules are fairly aggregated and broadcast to all the clients to maximize their consensus, and DSE modules are personalized for each client via similarity-aware aggregation to erase their domain skew differently. Comprehensive experiments were conducted on three datasets to confirm the advantages of our method in terms of accuracy, efficiency, and generalizability.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Federated_Learning_with_Domain_Shift_Eraser_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Federated_Learning_with_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zheng and Wang, Zihui and Wang, Zheng and Fan, Xiaoliang and Wang, Cheng},
    title     = {Federated Learning with Domain Shift Eraser},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4978-4987}
}

raw_affiliation:
[{"affiliation": "Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, China", "city": "Xiamen", "country": "China"}, {"affiliation": "Key Laboratory of Multimedia Trusted Perception and Efficient Computing,\nMinistry of Education of China, School of Informatics, Xiamen University, China", "city": "Xiamen", "country": "China"}, {"affiliation": "Shanghai Innovation Institution", "city": "Shanghai", "country": "China"}, {"affiliation": "School of Informatic, Xiamen University, China", "city": "Xiamen", "country": "China"}, {"affiliation": "Peng Cheng Laboratory, Shenzhen, China", "city": "Shenzhen", "country": "China"}]

affiliations:
["Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, China", "Key Laboratory of Multimedia Trusted Perception and Efficient Computing,\nMinistry of Education of China, School of Informatics, Xiamen University, China", "Shanghai Innovation Institution", "School of Informatic, Xiamen University, China", "Peng Cheng Laboratory, Shenzhen, China"]


title: Cross-Modal Distillation for 2D/3D Multi-Object Discovery from 2D Motion
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24529-24538
affiliations: ["Universit\u00b4e Paris-Saclay, CEA, List, F-91120, Palaiseau, France"]
cities: ["Palaiseau"]
countries: ["France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lahlali_Cross-Modal_Distillation_for_2D3D_Multi-Object_Discovery_from_2D_Motion_CVPR_2025_paper.html

authors:
Saad Lahlali, Sandra Kara, Hejer Ammar, Florian Chabot, Nicolas Granger, Hervé Le Borgne, Quoc-Cuong Pham

abstract:
Object discovery, which refers to the task of localizing objects without human annotations, has gained significant attention in 2D image analysis. However, despite this growing interest, it remains under-explored in 3D data, where approaches rely exclusively on 3D motion, despite its several challenges. In this paper, we present a novel framework that leverages advances in 2D object discovery which are based on 2D motion to exploit the advantages of such motion cues being more flexible and generalizable and to bridge the gap between 2D and 3D modalities.Our primary contributions are twofold: (i) we introduce DIOD-3D, the first baseline for multi-object discovery in 3D data using 2D motion, incorporating scene completion as an auxiliary task to enable dense object localization from sparse input data; (ii) we develop xMOD, a cross-modal training framework that integrates 2D and 3D data while always using 2D motion cues. xMOD employs a teacher-student training paradigm across the two modalities to mitigate confirmation bias by leveraging the domain gap. During inference, the model supports both RGB-only and point cloud-only inputs. Additionally, we propose a late-fusion technique tailored to our pipeline that further enhances performance when both modalities are available at inference.We evaluate our approach extensively on synthetic (TRIP-PD) and challenging real-world datasets (KITTI and Waymo). Notably, our approach yields a substantial performance improvement compared with the 2D object discovery state-of-the-art on all datasets with gains ranging from +8.7 to +15.1 in F1@50 score.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lahlali_Cross-Modal_Distillation_for_2D3D_Multi-Object_Discovery_from_2D_Motion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lahlali_Cross-Modal_Distillation_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lahlali_2025_CVPR,
    author    = {Lahlali, Saad and Kara, Sandra and Ammar, Hejer and Chabot, Florian and Granger, Nicolas and Le Borgne, Herv\'e and Pham, Quoc-Cuong},
    title     = {Cross-Modal Distillation for 2D/3D Multi-Object Discovery from 2D Motion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24529-24538}
}

raw_affiliation:
[{"affiliation": "Universit\u00b4e Paris-Saclay, CEA, List, F-91120, Palaiseau, France", "city": "Palaiseau", "country": "France"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7763-7772
cities: ["Hong Kong", "Dongguan", "Shenzhen", "Shenzhen", "Hong Kong"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cai_DiTCtrl_Exploring_Attention_Control_in_Multi-Modal_Diffusion_Transformer_for_Tuning-Free_CVPR_2025_paper.html

title:
DiTCtrl: Exploring Attention Control in Multi-Modal Diffusion Transformer for Tuning-Free Multi-Prompt Longer Video Generation

authors:
Minghong Cai, Xiaodong Cun, Xiaoyu Li, Wenze Liu, Zhaoyang Zhang, Yong Zhang, Ying Shan, Xiangyu Yue

abstract:
Sora-like video generation models have achieved remarkable progress with a Multi-Modal Diffusion Transformer (MM-DiT) architecture. However, the current video generation models predominantly focus on single-prompt, struggling to generate coherent scenes with multiple sequential prompts that better reflect real-world dynamic scenarios. While some pioneering works have explored multi-prompt video generation, they face significant challenges including strict training data requirements, weak prompt following, and unnatural transitions. To address these problems, we propose DiTCtrl, a training-free multi-prompt video generation method under MM-DiT architectures for the first time. Our key idea is to take the multi-prompt video generation task as temporal video editing with smooth transitions. To achieve this goal, we first analyze MM-DiT's attention mechanism, finding that the 3D full attention behaves similarly to that of the cross/self-attention blocks in the UNet-like diffusion models, enabling mask-guided precise semantic control across different prompts with attention sharing for multi-prompt video generation. Based on our careful design, the video generated by DiTCtrl achieves smooth transitions and consistent object motion given multiple sequential prompts without additional training. Besides, we also present MPVBench, a new benchmark specially designed for multi-prompt video generation to evaluate the performance of multi-prompt generation. Extensive experiments demonstrate that our method achieves state-of-the-art performance without additional training.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cai_DiTCtrl_Exploring_Attention_Control_in_Multi-Modal_Diffusion_Transformer_for_Tuning-Free_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cai_DiTCtrl_Exploring_Attention_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cai_2025_CVPR,
    author    = {Cai, Minghong and Cun, Xiaodong and Li, Xiaoyu and Liu, Wenze and Zhang, Zhaoyang and Zhang, Yong and Shan, Ying and Yue, Xiangyu},
    title     = {DiTCtrl: Exploring Attention Control in Multi-Modal Diffusion Transformer for Tuning-Free Multi-Prompt Longer Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7763-7772}
}

raw_affiliation:
[{"affiliation": "MMLab, CUHK", "city": "Hong Kong", "country": "China"}, {"affiliation": "GVC Lab, Great Bay University", "city": "Dongguan", "country": "China"}, {"affiliation": "ARC Lab, Tencent PCG", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tencent AI Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "SHIAE, CUHK", "city": "Hong Kong", "country": "China"}]

affiliations:
["MMLab, CUHK", "GVC Lab, Great Bay University", "ARC Lab, Tencent PCG", "Tencent AI Lab", "SHIAE, CUHK"]


title: Bridge Frame and Event: Common Spatiotemporal Fusion for High-Dynamic Scene Optical Flow
authors: Hanyu Zhou, Haonan Wang, Haoyue Liu, Yuxing Duan, Yi Chang, Luxin Yan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27904-27913
cities: ["Wuhan", "Singapore"]
countries: ["China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Bridge_Frame_and_Event_Common_Spatiotemporal_Fusion_for_High-Dynamic_Scene_CVPR_2025_paper.html

abstract:
High-dynamic scene optical flow is a challenging task, which suffers spatial blur and temporal discontinuous motion due to large displacement in frame imaging, thus deteriorating the spatiotemporal feature of optical flow. Typically, existing methods mainly introduce event camera to directly fuse the spatiotemporal features between the two modalities. However, this direct fusion is ineffective, since there exists a large gap due to the heterogeneous data representation between frame and event modalities. To address this issue, we explore a common-latent space as an intermediate bridge to mitigate the modality gap. In this work, we propose a novel common spatiotemporal fusion between frame and event modalities for high-dynamic scene optical flow, including visual boundary localization and motion correlation fusion. Specifically, in visual boundary localization, we figure out that frame and event share the similar spatiotemporal gradients, whose similarity distribution is consistent with the extracted boundary distribution. This motivates us to design the common spatiotemporal gradient to constrain the reference boundary localization. In motion correlation fusion, we discover that the frame-based motion possesses spatially dense but temporally discontinuous correlation, while the event-based motion has spatially sparse but temporally continuous correlation. This inspires us to use the reference boundary to guide the complementary motion knowledge fusion between the two modalities. Moreover, common spatiotemporal fusion can not only relieve the cross-modal feature discrepancy, but also make the fusion process interpretable for dense and continuous optical flow. Extensive experiments have been performed to verify the superiority of the proposed method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Bridge_Frame_and_Event_Common_Spatiotemporal_Fusion_for_High-Dynamic_Scene_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Bridge_Frame_and_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Hanyu and Wang, Haonan and Liu, Haoyue and Duan, Yuxing and Chang, Yi and Yan, Luxin},
    title     = {Bridge Frame and Event: Common Spatiotemporal Fusion for High-Dynamic Scene Optical Flow},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27904-27913}
}

raw_affiliation:
[{"affiliation": "National Key Lab of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "School of Computing, National University of Singapore", "city": "Singapore", "country": "Singapore"}]

affiliations:
["National Key Lab of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology", "School of Computing, National University of Singapore"]


title: EVPGS: Enhanced View Prior Guidance for Splatting-based Extrapolated View Synthesis
authors: Jiahe Li, Feiyu Wang, Xiaochao Qu, Chengjing Wu, Luoqi Liu, Ting Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16398-16407
raw_affiliation: [{"affiliation": "MT Lab, Meitu Inc.", "city": "Beijing", "country": "China"}]
affiliations: ["MT Lab, Meitu Inc."]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_EVPGS_Enhanced_View_Prior_Guidance_for_Splatting-based_Extrapolated_View_Synthesis_CVPR_2025_paper.html

abstract:
Gaussian Splatting (GS)-based methods rely on sufficient training view coverage and perform synthesis on interpolated views. In this work, we tackle the more challenging and underexplored Extrapolated View Synthesis (EVS) task. Here we enable GS-based models trained with limited view coverage to generalize well to extrapolated views. To achieve our goal, we propose a view augmentation framework to guide training through a coarse-to-fine process. At the coarse stage, we reduce rendering artifacts due to insufficient view coverage by introducing a regularization strategy at both appearance and geometry levels. At the fine stage, we generate reliable view priors to provide further training guidance. To this end, we incorporate an occlusion awareness into the view prior generation process, and refine the view priors with the aid of coarse stage output. We call our framework Enhanced View Prior Guidance for Splatting (EVPGS). To comprehensively evaluate EVPGS on the EVS task, we collect a real-world dataset called Merchandise3D dedicated to the EVS scenario. Experiments on three datasets including both real and synthetic demonstrate EVPGS achieves state-of-the-art performance, while improving synthesis quality at extrapolated views for GS-based methods both qualitatively and quantitatively. We will make our code, dataset, and models public.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_EVPGS_Enhanced_View_Prior_Guidance_for_Splatting-based_Extrapolated_View_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_EVPGS_Enhanced_View_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Jiahe and Wang, Feiyu and Qu, Xiaochao and Wu, Chengjing and Liu, Luoqi and Liu, Ting},
    title     = {EVPGS: Enhanced View Prior Guidance for Splatting-based Extrapolated View Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16398-16407}
}


authors: Yawen Shao, Wei Zhai, Yuhang Yang, Hongchen Luo, Yang Cao, Zheng-Jun Zha
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17326-17336
affiliations: ["University of Science and Technology of China", "Northeastern University"]
cities: ["Hefei", "Shenyang"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shao_GREAT_Geometry-Intention_Collaborative_Inference_for_Open-Vocabulary_3D_Object_Affordance_Grounding_CVPR_2025_paper.html

title:
GREAT: Geometry-Intention Collaborative Inference for Open-Vocabulary 3D Object Affordance Grounding

abstract:
Open-Vocabulary 3D object affordance grounding aims to anticipate "action possibilities" regions on 3D objects with arbitrary instructions, which is crucial for robots to generically perceive real scenarios and respond to operational changes. Existing methods focus on combining images or languages that depict interactions with 3D geometries to introduce external interaction priors. However, they are still vulnerable to a limited semantic space by failing to leverage implied invariant geometries and potential interaction intentions. Normally, humans address complex tasks through multi-step reasoning and respond to diverse situations by leveraging associative and analogical thinking. In light of this, we propose GREAT (Geometry-Intention Collaborative Inference) for Open-Vocabulary 3D Object Affordance Grounding, a novel framework that mines the object invariant geometry attributes and performs analogically reason in potential interaction scenarios to form affordance knowledge, fully combining the knowledge with both geometries and visual contents to ground 3D object affordance. Besides, we introduce the Point Image Affordance Dataset v2 (PIADv2), the largest 3D object affordance dataset at present to support the task. Extensive experiments demonstrate the effectiveness and superiority of GREAT. The code and dataset are available at https://yawen-shao.github.io/GREAT/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shao_GREAT_Geometry-Intention_Collaborative_Inference_for_Open-Vocabulary_3D_Object_Affordance_Grounding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shao_GREAT_Geometry-Intention_Collaborative_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shao_2025_CVPR,
    author    = {Shao, Yawen and Zhai, Wei and Yang, Yuhang and Luo, Hongchen and Cao, Yang and Zha, Zheng-Jun},
    title     = {GREAT: Geometry-Intention Collaborative Inference for Open-Vocabulary 3D Object Affordance Grounding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17326-17336}
}

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


title: Link to the Past: Temporal Propagation for Fast 3D Human Reconstruction from Monocular Video
authors: Matthew Marchellus, Nadhira Noor, In Kyu Park
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6190-6199
affiliations: ["Department of Electrical and Computer Engineering, Inha University"]
cities: ["Incheon"]
countries: ["Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Marchellus_Link_to_the_Past_Temporal_Propagation_for_Fast_3D_Human_CVPR_2025_paper.html

abstract:
Fast 3D clothed human reconstruction from monocular video remains a significant challenge in computer vision, particularly in balancing computational efficiency with reconstruction quality. Current approaches are either focused on static image reconstruction but too computationally intensive, or achieve high quality through per-video optimization that requires minutes to hours of processing, making them unsuitable for real-time applications. To this end, we present TemPoFast3D, a novel method that leverages temporal coherency of human appearance to reduce redundant computation while maintaining reconstruction quality. Our approach is a "plug-and play" solution that uniquely transforms pixel-aligned reconstruction networks to handle continuous video streams by maintaining and refining a canonical appearance representation through efficient coordinate mapping. Extensive experiments demonstrate that TemPoFast3D matches or exceeds state-of-the-art methods across standard metrics while providing high-quality textured reconstruction across diverse pose and appearance, with a maximum speed of 12 FPS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Marchellus_Link_to_the_Past_Temporal_Propagation_for_Fast_3D_Human_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Marchellus_Link_to_the_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Marchellus_2025_CVPR,
    author    = {Marchellus, Matthew and Noor, Nadhira and Park, In Kyu},
    title     = {Link to the Past: Temporal Propagation for Fast 3D Human Reconstruction from Monocular Video},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6190-6199}
}

raw_affiliation:
[{"affiliation": "Department of Electrical and Computer Engineering, Inha University", "city": "Incheon", "country": "Korea"}]


title: Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce Classification
authors: Yanghao Wang, Long Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25560-25569
affiliations: ["The Hong Kong University of Science and Technology"]
cities: ["Hong Kong"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Inversion_Circle_Interpolation_Diffusion-based_Image_Augmentation_for_Data-scarce_Classification_CVPR_2025_paper.html

abstract:
Data Augmentation (DA), i.e., synthesizing faithful and diverse samples to expand the original training set, is a prevalent and effective strategy to improve the performance of various data-scarce tasks. With the powerful image generation ability, diffusion-based DA has shown strong performance gains on different image classification benchmarks. In this paper, we analyze today's diffusion-based DA methods, and argue that they cannot take account of both faithfulness and diversity, which are two critical keys for generating high-quality samples and boosting classification performance. To this end, we propose a novel Diffusion-based DA method: Diff-II. Specifically, it consists of three steps: 1) Category concepts learning: Learning concept embeddings for each category. 2) Inversion interpolation: Calculating the inversion for each image, and conducting circle interpolation for two randomly sampled inversions from the same category. 3) Two-stage denoising: Using different prompts to generate synthesized images in a coarse-to-fine manner. Extensive experiments on various data-scarce image classification tasks (e.g., few-shot, long-tailed, and out-of-distribution classification) have demonstrated its effectiveness over state-of-the-art diffusion-based DA methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Inversion_Circle_Interpolation_Diffusion-based_Image_Augmentation_for_Data-scarce_Classification_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Inversion_Circle_Interpolation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yanghao and Chen, Long},
    title     = {Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce Classification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25560-25569}
}

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


authors: Jiate Li, Meng Pang, Yun Dong, Binghui Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5020-5029
cities: ["Chicago", "Nanchang", "Milwaukee"]
countries: ["USA", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Deterministic_Certification_of_Graph_Neural_Networks_against_Graph_Poisoning_Attacks_CVPR_2025_paper.html

title:
Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary Perturbations

abstract:
Graph neural networks (GNNs) are becoming the de facto method to learn on the graph data and have achieved the state-of-the-art on node and graph classification tasks. However, recent works show GNNs are vulnerable to  training-time poisoning attacks -- marginally perturbing edges, nodes, and node features of training graphs can largely degrade the GNN performance. Most previous defenses against such attacks are empirical and are soon broken by adaptive / stronger ones. A few provable defenses provide robustness guarantees, but have large gaps when applied in practice: 1) all restrict the attacker's capability to only one type of perturbation; 2) all are designed for a particular GNN task; and 3) their  robustness guarantees are not 100% accurate. In this work, we bridge all these gaps by developing PGNNCert, the first certified defense of GNNs against poisoning attacks under arbitrary (edge, node, and node feature) perturbations with deterministic (100% accurate) robustness guarantees. PGNNCert is also applicable to the two most widely-studied node and graph classification tasks. Extensive evaluations on multiple node and graph classification datasets and GNNs demonstrate the effectiveness of PGNNCert to provably defend against arbitrary poisoning perturbations. PGNNCert is also shown to significantly outperform the state-of-the-art certified defenses against edge perturbation or node perturbation during GNN training.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Deterministic_Certification_of_Graph_Neural_Networks_against_Graph_Poisoning_Attacks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Deterministic_Certification_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Jiate and Pang, Meng and Dong, Yun and Wang, Binghui},
    title     = {Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary Perturbations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5020-5029}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science, Illinois Institute of Technology, Chicago, USA", "city": "Chicago", "country": "USA"}, {"affiliation": "School of Mathematics and Computer Sciences, Nanchang University, Nanchang, China", "city": "Nanchang", "country": "China"}, {"affiliation": "Department of Humanities, Social Science, and Communication, MSOE, Milwaukee, USA", "city": "Milwaukee", "country": "USA"}]

affiliations:
["Department of Computer Science, Illinois Institute of Technology, Chicago, USA", "School of Mathematics and Computer Sciences, Nanchang University, Nanchang, China", "Department of Humanities, Social Science, and Communication, MSOE, Milwaukee, USA"]


title: A3: Few-shot Prompt Learning of Unlearnable Examples with Cross-Modal Adversarial Feature Alignment
authors: Xuan Wang, Xitong Gao, Dongping Liao, Tianrui Qin, Yu-liang Lu, Cheng-zhong Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9507-9516
cities: ["Changsha", "Shenzhen", "Shenzhen", "Macau", "Unknown", "Changsha", "Macau"]
countries: ["China", "China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_A3_Few-shot_Prompt_Learning_of_Unlearnable_Examples_with_Cross-Modal_Adversarial_CVPR_2025_paper.html

abstract:
In the age of pervasive machine learning applications, protecting digital content from unauthorized use has become a pressing concern. Unlearnable examples (UEs)--data modified with imperceptible perturbations to inhibit model training while preserving human usability--have emerged as a promising approach. However, existing UE methods assume unauthorized trainers have extensive exposure to UEs or that models are trained from scratch, which may not hold in practical scenarios, This paper investigates the effectiveness of UEs under the few-shot learning paradigm, pitching it against prompt learning (PL) models that leverage pretrained vision-language models (VLMs), like CLIP, capable of generalizing to new classes with minimal data. To address this, we introduce an adaptive UE framework to generate unlearnable examples that specifically target the PL process. In addition, we propose a novel UE countermeasure, A3, with cross-modal adversarial feature alignment, specifically designed to circumvent UEs under few-shot PL. Experimental evaluations on 7 datasets show that A3 outperforms existing PL methods, achieving up to 33% higher performance in learning from UEs. For example, in the scenario involving l_infinity-bounded EM perturbations, A3 has an average harmonic mean accuracy across 7 datasets of 82.43%, compared to CoCoOp's baseline of 65.47%. Our findings highlight the limitations of existing UEs against PL and lay the foundation for future data protection mechanisms.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_A3_Few-shot_Prompt_Learning_of_Unlearnable_Examples_with_Cross-Modal_Adversarial_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_A3_Few-shot_Prompt_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Xuan and Gao, Xitong and Liao, Dongping and Qin, Tianrui and Lu, Yu-liang and Xu, Cheng-zhong},
    title     = {A3: Few-shot Prompt Learning of Unlearnable Examples with Cross-Modal Adversarial Feature Alignment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9507-9516}
}

raw_affiliation:
[{"affiliation": "Anhui Key Lab of CSSAE, National University of Defense Technology", "city": "Changsha", "country": "China"}, {"affiliation": "Shenzhen Institutes of Advanced Technology, CAS", "city": "Shenzhen", "country": "China"}, {"affiliation": "Shenzhen University of Advanced Technology", "city": "Shenzhen", "country": "China"}, {"affiliation": "State Key Lab of IoTSC, CIS Dept, University of Macau", "city": "Macau", "country": "China"}, {"affiliation": "OPPO Research Institute", "city": "Unknown", "country": "China"}, {"affiliation": "Anhui Key Lab of CSSAE, National University of Defense Technology", "city": "Changsha", "country": "China"}, {"affiliation": "State Key Lab of IoTSC, CIS Dept, University of Macau", "city": "Macau", "country": "China"}]

affiliations:
["Anhui Key Lab of CSSAE, National University of Defense Technology", "Shenzhen Institutes of Advanced Technology, CAS", "Shenzhen University of Advanced Technology", "State Key Lab of IoTSC, CIS Dept, University of Macau", "OPPO Research Institute", "Anhui Key Lab of CSSAE, National University of Defense Technology", "State Key Lab of IoTSC, CIS Dept, University of Macau"]


authors: Baixuan Lv, Yaohua Zha, Tao Dai, Xue Yuerong, Ke Chen, Shu-Tao Xia
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12413-12422
affiliations: ["Tsinghua University", "Pengcheng Laboratory", "Shenzhen University"]
cities: ["Beijing", "Shenzhen", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lv_Adapting_Pre-trained_3D_Models_for_Point_Cloud_Video_Understanding_via_CVPR_2025_paper.html

title:
Adapting Pre-trained 3D Models for Point Cloud Video Understanding via Cross-frame Spatio-temporal Perception

abstract:
Point cloud video understanding is becoming increasingly important in fields such as robotics, autonomous driving, and augmented reality, as they can accurately represent object motion and environmental changes. Despite the progress made in self-supervised learning methods for point cloud video understanding, the limited availability of 4D data and the high computational cost of training 4D-specific models remain significant obstacles. In this paper, we investigate the potential of transferring pre-trained static 3D point cloud models to the 4D domain, identifying the limitations of static models that capture only spatial information while neglecting temporal dynamics. To address this, we propose a novel Cross-frame Spatio-temporal Adaptation (CSA) strategy by introducing the Point Tube Adapter as the embedding layer and the Geometric Constraint Temporal Adapter (GCTA) to enforce temporal consistency across frames. This strategy extracts both short-term and long-term temporal dynamics, effectively integrating them with spatial features and enriching the model's understanding of temporal changes in point cloud videos. Extensive experiments on 3D action and gesture recognition tasks demonstrate that our method achieves state-of-the-art performance, establishing its effectiveness for point cloud video understanding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lv_Adapting_Pre-trained_3D_Models_for_Point_Cloud_Video_Understanding_via_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lv_Adapting_Pre-trained_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lv_2025_CVPR,
    author    = {Lv, Baixuan and Zha, Yaohua and Dai, Tao and Yuerong, Xue and Chen, Ke and Xia, Shu-Tao},
    title     = {Adapting Pre-trained 3D Models for Point Cloud Video Understanding via Cross-frame Spatio-temporal Perception},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12413-12422}
}

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


authors: Kyungho Bae, Jinhyung Kim, Sihaeng Lee, Soonyoung Lee, Gunhee Lee, Jinwoo Choi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13744-13753
affiliations: ["Kyung Hee University", "LG AI Research"]
cities: ["Seoul", "Seoul"]
countries: ["South Korea", "South Korea"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bae_MASH-VLM_Mitigating_Action-Scene_Hallucination_in_Video-LLMs_through_Disentangled_Spatial-Temporal_Representations_CVPR_2025_paper.html

title:
MASH-VLM: Mitigating Action-Scene Hallucination in Video-LLMs through Disentangled Spatial-Temporal Representations

abstract:
In this work, we tackle action-scene hallucination in Video Large Language Models (Video-LLMs), where models incorrectly predict actions based on the scene context or scenes based on observed actions. We observe that existing Video-LLMs often suffer from action-scene hallucination due to two main factors. First, existing Video-LLMs intermingle spatial and temporal features by applying an attention operation across all tokens. Second, they use the standard Rotary Position Embedding (RoPE), which causes the text tokens to overemphasize certain types of tokens depending on their sequential orders. To address these issues, we introduce MASH-VLM, Mitigating Action-Scene Hallucination in Video-LLMs through disentangled spatial-temporal representations. Our approach includes two key innovations: (1) DST-attention, a novel attention mechanism that disentangles the spatial and temporal tokens within the LLM by using masked attention to restrict direct interactions between the spatial and temporal tokens; (2) Harmonic-RoPE, which extends the dimensionality of the positional IDs, allowing the spatial and temporal tokens to maintain balanced positions relative to the text tokens. To evaluate the action-scene hallucination in Video-LLMs, we introduce the UNSCENE benchmark with 1,320 videos and 4,078 QA pairs. Extensive experiments demonstrate that MASH-VLM achieves state-of-the-art results on the UNSCENE benchmark, as well as on existing video understanding benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bae_MASH-VLM_Mitigating_Action-Scene_Hallucination_in_Video-LLMs_through_Disentangled_Spatial-Temporal_Representations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bae_MASH-VLM_Mitigating_Action-Scene_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Bae_2025_CVPR,
    author    = {Bae, Kyungho and Kim, Jinhyung and Lee, Sihaeng and Lee, Soonyoung and Lee, Gunhee and Choi, Jinwoo},
    title     = {MASH-VLM: Mitigating Action-Scene Hallucination in Video-LLMs through Disentangled Spatial-Temporal Representations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13744-13753}
}

raw_affiliation:
[{"affiliation": "Kyung Hee University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "LG AI Research", "city": "Seoul", "country": "South Korea"}]


title: UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13561-13570
cities: ["Taipei", "Taipei", "Cambridge"]
countries: ["Taiwan", "Taiwan", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lai_UWAV_Uncertainty-weighted_Weakly-supervised_Audio-Visual_Video_Parsing_CVPR_2025_paper.html

authors:
Yung-Hsuan Lai, Janek Ebbers, Yu-Chiang Frank Wang, François Germain, Michael Jeffrey Jones, Moitreya Chatterjee

abstract:
Audio-Visual Video Parsing (AVVP) entails the challenging task of localizing both uni-modal events (i.e., those occurring exclusively in either the visual or acoustic modality of a video) and multi-modal events (i.e., those occurring in both modalities concurrently). Moreover, the prohibitive cost of annotating training data with the class labels of all these events, along with their start and end times, imposes constraints on the scalability of AVVP techniques unless they can be trained in a weakly-supervised setting, where only modality-agnostic, video-level labels are available in the training data. To this end, recently proposed approaches seek to generate segment-level pseudo-labels to better guide model training. However, the absence of inter-segment dependencies when generating these pseudo-labels and the general bias towards predicting labels that are absent in a segment limit their performance. This work proposes a novel approach towards overcoming these weaknesses called Uncertainty-weighted Weakly-supervised Audio-visual Video Parsing (UWAV). Additionally, our innovative approach factors in the uncertainty associated with these estimated pseudo-labels and incorporates a feature mixup based training regularization for improved training. Empirical results show that UWAV outperforms state-of-the-art methods for the AVVP task on multiple metrics, across two different datasets, attesting to its effectiveness and generalizability.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lai_UWAV_Uncertainty-weighted_Weakly-supervised_Audio-Visual_Video_Parsing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lai_UWAV_Uncertainty-weighted_Weakly-supervised_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lai_2025_CVPR,
    author    = {Lai, Yung-Hsuan and Ebbers, Janek and Wang, Yu-Chiang Frank and Germain, Fran\c{c}ois and Jones, Michael Jeffrey and Chatterjee, Moitreya},
    title     = {UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13561-13570}
}

raw_affiliation:
[{"affiliation": "Graduate Institute of Communication Engineering, National Taiwan University", "city": "Taipei", "country": "Taiwan"}, {"affiliation": "NVIDIA", "city": "Taipei", "country": "Taiwan"}, {"affiliation": "Mitsubishi Electric Research Labs (MERL)", "city": "Cambridge", "country": "USA"}]

affiliations:
["Graduate Institute of Communication Engineering, National Taiwan University", "NVIDIA", "Mitsubishi Electric Research Labs (MERL)"]


title: Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning
authors: Jing Zhu, Yuhang Zhou, Shengyi Qian, Zhongmou He, Tong Zhao, Neil Shah, Danai Koutra
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14215-14224
affiliations: ["University of Michigan", "Snap Inc.", "University of Maryland"]
cities: ["Ann Arbor", "Los Angeles", "College Park"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Mosaic_of_Modalities_A_Comprehensive_Benchmark_for_Multimodal_Graph_Learning_CVPR_2025_paper.html

abstract:
Graph machine learning has made significant strides in recent years, yet the integration of visual information with graph structure and its potential for improving performance in downstream tasks remains an underexplored area. To address this critical gap, we introduce the Multimodal Graph Benchmark (MM-GRAPH), a pioneering benchmark that incorporates both visual and textual information into graph learning tasks. MM-GRAPH extends beyond existing text-attributed graph benchmarks, offering a more comprehensive evaluation framework for multimodal graph learning. Our benchmark comprises seven diverse datasets of varying scales (ranging from thousands to millions of edges), designed to assess algorithms across different tasks in real-world scenarios. These datasets feature rich multimodal node attributes, including visual data, which enables a more holistic evaluation of various graph learning frameworks in complex, multimodal environments. To support advancements in this emerging field, we provide an extensive empirical study on various graph learning frameworks when presented with features from multiple modalities, particularly emphasizing the impact of visual information. This study offers valuable insights into the challenges and opportunities of integrating visual data into graph learning.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Mosaic_of_Modalities_A_Comprehensive_Benchmark_for_Multimodal_Graph_Learning_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Jing and Zhou, Yuhang and Qian, Shengyi and He, Zhongmou and Zhao, Tong and Shah, Neil and Koutra, Danai},
    title     = {Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14215-14224}
}

raw_affiliation:
[{"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "Snap Inc.", "city": "Los Angeles", "country": "USA"}, {"affiliation": "University of Maryland", "city": "College Park", "country": "USA"}]


title: TSP-Mamba: The Travelling Salesman Problem Meets Mamba for Image Super-resolution and Beyond
authors: Kun Zhou, Xinyu Lin, Jiangbo Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28134-28143
affiliations: ["SmartMore Corporation", "SSE, CUHK-Shenzhen"]
cities: ["Unknown", "Shenzhen"]
countries: ["Unknown", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_TSP-Mamba_The_Travelling_Salesman_Problem_Meets_Mamba_for_Image_Super-resolution_CVPR_2025_paper.html

abstract:
Recently, Mamba-based frameworks have achieved substantial advancements across diverse computer vision and NLP tasks, particularly in their capacity for reasoning over long-range information with linear complexity. However, the fixed 2D-to-1D scanning pattern overlooks the local structures of an image, limiting its effectiveness in aggregating 2D spatial information. While stacking additional Mamba layers can partially address this issue, it increases parameter intensity and constrains real-time application. In this work, we reconsider the local optimal scanning path in Mamba, enhancing the rigid and uniform 1D scan through the local shortest path theory, thus creating a structure-aware Mamba suited for lightweight single-image super-resolution. Specifically, we draw inspiration from the Traveling Salesman Problem (TSP) to establish a local optimal scanning path for improved structural 2D information utilization. Here, local patch aggregation occurs in a content-adaptive manner with minimal propagation cost. TSP-Mamba demonstrates substantial improvements over existing Mamba-based and Transformer-based architectures. For example, TSP-Mamba surpasses MambaIR by up to 0.7dB in lightweight SISR, with comparable parameters and very slightly extra computational demands (1-2 GFlops for 720P images).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_TSP-Mamba_The_Travelling_Salesman_Problem_Meets_Mamba_for_Image_Super-resolution_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_TSP-Mamba_The_Travelling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Kun and Lin, Xinyu and Lu, Jiangbo},
    title     = {TSP-Mamba: The Travelling Salesman Problem Meets Mamba for Image Super-resolution and Beyond},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28134-28143}
}

raw_affiliation:
[{"affiliation": "SmartMore Corporation", "city": "Unknown", "country": "Unknown"}, {"affiliation": "SSE, CUHK-Shenzhen", "city": "Shenzhen", "country": "China"}]


title: MVPaint: Synchronized Multi-View Diffusion for Painting Anything 3D
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 585-594
cities: ["Unknown", "Shanghai", "Unknown", "Singapore", "Beijing", "Hangzhou", "Unknown"]
countries: ["Unknown", "China", "China", "Singapore", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cheng_MVPaint_Synchronized_Multi-View_Diffusion_for_Painting_Anything_3D_CVPR_2025_paper.html

authors:
Wei Cheng, Juncheng Mu, Xianfang Zeng, Xin Chen, Anqi Pang, Chi Zhang, Zhibin Wang, Bin Fu, Gang Yu, Ziwei Liu, Liang Pan

abstract:
Texturing is a crucial step in the 3D asset production workflow, which enhances the visual appeal and diversity of 3D assets. Despite recent advancements in Text-to-Texture (T2T) generation, existing methods often yield subpar results, primarily due to local discontinuities, inconsistencies across multiple views, and their heavy dependence on UV unwrapping outcomes. To tackle these challenges, we propose a novel generation-refinement 3D texturing framework called MVPaint, which can generate high-resolution, seamless textures while emphasizing multi-view consistency. MVPaint mainly consists of three key modules. 1) Synchronized Multi-view Generation (SMG). Given a 3D mesh model, MVPaint first simultaneously generates multi-view images by employing an SMG model, which leads to coarse texturing results with unpainted parts due to missing observations. 2) Spatial-aware 3D Inpainting (S3I). To ensure complete 3D texturing, we introduce the S3I method, specifically designed to effectively texture previously unobserved areas. 3) UV Refinement (UVR). Furthermore, MVPaint employs a UVR module to improve the texture quality in the UV space, which first performs a UV-space Super-Resolution, followed by a Spatial-aware Seam-Smoothing algorithm for revising spatial texturing discontinuities caused by UV unwrapping. Moreover, we establish two T2T evaluation benchmarks: the Objaverse T2T benchmark and the GSO T2T benchmark, based on selected high-quality 3D meshes from the Objaverse dataset and the entire GSO dataset, respectively. Extensive experimental results demonstrate that MVPaint surpasses existing state-of-the-art methods. Notably, MVPaint could generate high-fidelity textures with minimal Janus issues and highly enhanced cross-view consistency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cheng_MVPaint_Synchronized_Multi-View_Diffusion_for_Painting_Anything_3D_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Cheng_2025_CVPR,
    author    = {Cheng, Wei and Mu, Juncheng and Zeng, Xianfang and Chen, Xin and Pang, Anqi and Zhang, Chi and Wang, Zhibin and Fu, Bin and Yu, Gang and Liu, Ziwei and Pan, Liang},
    title     = {MVPaint: Synchronized Multi-View Diffusion for Painting Anything 3D},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {585-594}
}

raw_affiliation:
[{"affiliation": "StepFun", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Tencent PCG", "city": "Unknown", "country": "China"}, {"affiliation": "S-Lab, NTU", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "AGI Lab, Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "ByteDance", "city": "Unknown", "country": "China"}]

affiliations:
["StepFun", "Shanghai AI Laboratory", "Tencent PCG", "S-Lab, NTU", "Tsinghua University", "AGI Lab, Westlake University", "ByteDance"]


title: FirePlace: Geometric Refinements of LLM Common Sense Reasoning for 3D Object Placement
authors: Ian Huang, Yanan Bao, Karen Truong, Howard Zhou, Cordelia Schmid, Leonidas Guibas, Alireza Fathi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13466-13476
affiliations: ["Stanford University", "Google DeepMind"]
cities: ["Stanford", "Mountain View"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_FirePlace_Geometric_Refinements_of_LLM_Common_Sense_Reasoning_for_3D_CVPR_2025_paper.html

abstract:
Scene generation with 3D assets presents a complex challenge, requiring both high-level semantic understanding and low-level geometric reasoning. While Multimodal Large Language Models (MLLMs) excel at semantic tasks, their application to 3D scene generation is hindered by their limited grounding on 3D geometry. In this paper, we investigate how to best work with MLLMs in an object placement task. Towards this goal, we introduce a novel framework, FirePlace, that applies existing MLLMs in (1) 3D geometric reasoning and the extraction of relevant geometric details from the 3D scene, (2) constructing and solving geometric constraints on the extracted low-level geometry, and (3) pruning for final placements that conform to common sense. By combining geometric reasoning with real-world understanding of MLLMs, our method can propose object placements that satisfy both geometric constraints as well as high-level semantic common-sense considerations. Our experiments show that these capabilities allow our method to place objects more effectively in complex scenes with intricate geometry, surpassing the quality of prior work.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_FirePlace_Geometric_Refinements_of_LLM_Common_Sense_Reasoning_for_3D_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_FirePlace_Geometric_Refinements_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Ian and Bao, Yanan and Truong, Karen and Zhou, Howard and Schmid, Cordelia and Guibas, Leonidas and Fathi, Alireza},
    title     = {FirePlace: Geometric Refinements of LLM Common Sense Reasoning for 3D Object Placement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13466-13476}
}

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


title: RENO: Real-Time Neural Compression for 3D LiDAR Point Clouds
authors: Kang You, Tong Chen, Dandan Ding, M. Salman Asif, Zhan Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22172-22181
affiliations: ["Nanjing University", "Hangzhou Normal University", "University of California Riverside"]
cities: ["Nanjing", "Hangzhou", "Riverside"]
countries: ["China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/You_RENO_Real-Time_Neural_Compression_for_3D_LiDAR_Point_Clouds_CVPR_2025_paper.html

abstract:
Despite the substantial advancements demonstrated by learning-based neural models in the LiDAR Point Cloud Compression (LPCC) task, realizing real-time compression--an indispensable criterion for numerous industrial applications--remains a formidable challenge. This paper proposes RENO, the first real-time neural codec for 3D LiDAR point clouds, achieving superior performance with a lightweight model. RENO skips the octree construction and directly builds upon the multiscale sparse tensor representation. Instead of the multi-stage inferring, RENO devises sparse occupancy codes, which exploit cross-scale correlation and derive voxels' occupancy in a one-shot manner, greatly saving processing time. Experimental results demonstrate that the proposed RENO achieves real-time coding speed, 10 fps at 14-bit depth on a desktop platform (e.g., one RTX 3090 GPU) for both encoding and decoding processes, while providing 12.25% and 48.34% bit-rate savings compared to G-PCCv23 and Draco, respectively, at a similar quality. RENO model size is merely 1MB, making it attractive for practical applications. The source code is available at https://github.com/NJUVISION/RENO.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/You_RENO_Real-Time_Neural_Compression_for_3D_LiDAR_Point_Clouds_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/You_RENO_Real-Time_Neural_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{You_2025_CVPR,
    author    = {You, Kang and Chen, Tong and Ding, Dandan and Asif, M. Salman and Ma, Zhan},
    title     = {RENO: Real-Time Neural Compression for 3D LiDAR Point Clouds},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22172-22181}
}

raw_affiliation:
[{"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Hangzhou Normal University", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of California Riverside", "city": "Riverside", "country": "USA"}]


title: End-to-End Implicit Neural Representations for Classification
authors: Alexander Gielisse, Jan van Gemert
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18728-18737
raw_affiliation: [{"affiliation": "Delft University of Technology", "city": "Delft", "country": "Netherlands"}]
affiliations: ["Delft University of Technology"]
cities: ["Delft"]
countries: ["Netherlands"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gielisse_End-to-End_Implicit_Neural_Representations_for_Classification_CVPR_2025_paper.html

abstract:
Implicit neural representations (INRs)  such as NeRF and SIREN encode a signal in neural network parameters and show excellent results for signal reconstruction. Using INRs for downstream tasks, such as classification, is however not straightforward. Inherent symmetries in the parameters pose challenges and current works primarily focus on designing architectures that are equivariant to these symmetries. However, INR-based classification still significantly under-performs compared to pixel-based methods like CNNs. This work presents an end-to-end strategy for initializing SIRENs together with a learned learning-rate scheme, to yield representations that improve classification accuracy. We show that a simple, straightforward, Transformer model applied to a meta-learned SIREN, without incorporating explicit symmetry equivariances, outperforms the current state-of-the-art. On the CIFAR-10 SIREN classification task, we improve the state-of-the-art without augmentations from 38.8% to 59.6%, and from 63.4% to 64.7% with augmentations. We demonstrate scalability on the high-resolution Imagenette dataset achieving reasonable reconstruction quality with a classification accuracy of 60.8% and are the first to do INR classification on the full ImageNet-1K dataset where we achieve a SIREN classification performance of 23.6%. To the best of our knowledge, no other SIREN classification approach has managed to set a classification baseline for any high-resolution image dataset. Our code is available at https://github.com/SanderGielisse/MWT

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gielisse_End-to-End_Implicit_Neural_Representations_for_Classification_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Gielisse_2025_CVPR,
    author    = {Gielisse, Alexander and van Gemert, Jan},
    title     = {End-to-End Implicit Neural Representations for Classification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18728-18737}
}


title: ASAP: Advancing Semantic Alignment Promotes Multi-Modal Manipulation Detecting and Grounding
authors: Zhenxing Zhang, Yaxiong Wang, Lechao Cheng, Zhun Zhong, Dan Guo, Meng Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4005-4014
cities: ["Hefei", "Hefei"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_ASAP_Advancing_Semantic_Alignment_Promotes_Multi-Modal_Manipulation_Detecting_and_Grounding_CVPR_2025_paper.html

abstract:
We present ASAP, a new framework for detecting and grounding multi-modal media manipulation (DGM4).Upon thorough examination, we observe that accurate fine-grained cross-modal semantic alignment between the image and text is vital for accurately manipulation detection and grounding. While existing DGM4 methods pay rare attention to the cross-modal alignment, hampering the accuracy of manipulation detecting to step further. To remedy this issue, this work targets to advance the semantic alignment learning to promote this task. Particularly, we utilize the off-the-shelf Multimodal Large-Language Models (MLLMs) and Large Language Models (LLMs) to construct paired image-text pairs, especially for the manipulated instances. Subsequently, a cross-modal alignment learning is performed to enhance the semantic alignment. Besides the explicit auxiliary clues, we further design a Manipulation-Guided Cross Attention (MGCA) to provide implicit guidance for augmenting the manipulation perceiving. With the grounding truth available during training, MGCA encourages the model to concentrate more on manipulated components while downplaying normal ones, enhancing the model's ability to capture manipulations. Extensive experiments are conducted on the DGM4 dataset, the results demonstrate that our model can surpass the comparison method with a clear margin.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_ASAP_Advancing_Semantic_Alignment_Promotes_Multi-Modal_Manipulation_Detecting_and_Grounding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_ASAP_Advancing_Semantic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zhenxing and Wang, Yaxiong and Cheng, Lechao and Zhong, Zhun and Guo, Dan and Wang, Meng},
    title     = {ASAP: Advancing Semantic Alignment Promotes Multi-Modal Manipulation Detecting and Grounding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4005-4014}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Information Engineering, Hefei University of Technology, China", "city": "Hefei", "country": "China"}, {"affiliation": "Institute of Arti\ufb01cial Intelligence, Hefei Comprehensive National Science Center, China", "city": "Hefei", "country": "China"}]

affiliations:
["School of Computer Science and Information Engineering, Hefei University of Technology, China", "Institute of Arti\ufb01cial Intelligence, Hefei Comprehensive National Science Center, China"]


title: UNICL-SAM: Uncertainty-Driven In-Context Segmentation with Part Prototype Discovery
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20201-20211
cities: ["Hefei", "Redmond", "Beijing", "Hefei", "Shanghai", "Beijing"]
countries: ["China", "USA", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sheng_UNICL-SAM_Uncertainty-Driven_In-Context_Segmentation_with_Part_Prototype_Discovery_CVPR_2025_paper.html

authors:
Dianmo Sheng, Dongdong Chen, Zhentao Tan, Qiankun Liu, Qi Chu, Tao Gong, Bin Liu, Jing Han, Wenbin Tu, Shengwei Xu, Nenghai Yu

abstract:
Recent advancements in in-context segmentation generalists have demonstrated significant success in performing various image segmentation tasks using a limited number of labeled example images. However, real-world applications present challenges due to the variability of support examples, which often exhibit quality issues resulting from various sources and inaccurate labeling. How to extract more robust representations from these examples has always been one of the goals of in-context visual learning. In response, we propose UNICL-SAM, to better model the example distribution and extract robust representations to help in-context segmentation. We incorporate an uncertainty probabilistic module to quantify each example's reliability during both the training and testing phases. Utilizing this uncertainty estimation, we introduce an uncertainty-guided graph augmentation and feature refinement strategy, aimed at mitigating the impact of high-uncertainty regions to enhance the learning of robust representations. Subsequently, we construct prototypes for each example by aggregating part information, thereby creating reliable in-context instruction that effectively represents fine-grained local semantics. This approach serves as a valuable complement to traditional global pooling features. Experimental results demonstrate the effectiveness of the proposed framework, underscoring its potential for real-world applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sheng_UNICL-SAM_Uncertainty-Driven_In-Context_Segmentation_with_Part_Prototype_Discovery_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sheng_UNICL-SAM_Uncertainty-Driven_In-Context_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sheng_2025_CVPR,
    author    = {Sheng, Dianmo and Chen, Dongdong and Tan, Zhentao and Liu, Qiankun and Chu, Qi and Gong, Tao and Liu, Bin and Han, Jing and Tu, Wenbin and Xu, Shengwei and Yu, Nenghai},
    title     = {UNICL-SAM: Uncertainty-Driven In-Context Segmentation with Part Prototype Discovery},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20201-20211}
}

raw_affiliation:
[{"affiliation": "School of Cyber Science and Technology, USTC", "city": "Hefei", "country": "China"}, {"affiliation": "Microsoft GenAI", "city": "Redmond", "country": "USA"}, {"affiliation": "USTB", "city": "Beijing", "country": "China"}, {"affiliation": "Anhui Province Key Laboratory of Digital Security", "city": "Hefei", "country": "China"}, {"affiliation": "BoCom Research", "city": "Shanghai", "country": "China"}, {"affiliation": "BESTI", "city": "Beijing", "country": "China"}]

affiliations:
["School of Cyber Science and Technology, USTC", "Microsoft GenAI", "USTB", "Anhui Province Key Laboratory of Digital Security", "BoCom Research", "BESTI"]


title: Layered Motion Fusion: Lifting Motion Segmentation to 3D in Egocentric Videos
authors: Vadim Tschernezki, Diane Larlus, Iro Laina, Andrea Vedaldi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17637-17648
affiliations: ["Visual Geometry Group, University of Oxford", "NA VER LABS Europe"]
cities: ["Oxford", "Unknown"]
countries: ["United Kingdom", "Europe"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tschernezki_Layered_Motion_Fusion_Lifting_Motion_Segmentation_to_3D_in_Egocentric_CVPR_2025_paper.html

abstract:
Computer vision is largely based on 2D techniques, with 3D vision still relegated to a relatively narrow subset of applications. However, by building on recent advances in 3D models such as neural radiance fields, some authors have shown that 3D techniques can at last improve outputs extracted from independent 2D views, by fusing them into 3D and denoising them. This is particularly helpful in egocentric videos, where the camera motion is significant, but only under the assumption that the scene itself is static. In fact, as shown in the recent analysis conducted by EPIC Fields, 3D techniques are ineffective when it comes to studying dynamic phenomena, and, in particular, when segmenting moving objects. In this paper, we look into this issue in more detail. First, we propose to improve dynamic segmentation in 3D by fusing motion segmentation predictions from a 2D-based model into layered radiance fields (Layered Motion Fusion).  However, the high complexity of long, dynamic videos makes it challenging to capture the underlying geometric structure, and, as a result, hinders the fusion of motion cues into the (incomplete) scene geometry. We address this issue through test-time refinement, which helps the model to focus on specific frames, thereby reducing the data complexity. This results in a synergy between motion fusion and the refinement, and in turn leads to segmentation predictions of the 3D model that surpass the 2D baseline by a large margin. This demonstrates that 3D techniques can enhance 2D analysis even for dynamic phenomena in a challenging and realistic setting.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tschernezki_Layered_Motion_Fusion_Lifting_Motion_Segmentation_to_3D_in_Egocentric_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tschernezki_Layered_Motion_Fusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tschernezki_2025_CVPR,
    author    = {Tschernezki, Vadim and Larlus, Diane and Laina, Iro and Vedaldi, Andrea},
    title     = {Layered Motion Fusion: Lifting Motion Segmentation to 3D in Egocentric Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17637-17648}
}

raw_affiliation:
[{"affiliation": "Visual Geometry Group, University of Oxford", "city": "Oxford", "country": "United Kingdom"}, {"affiliation": "NA VER LABS Europe", "city": "Unknown", "country": "Europe"}]


title: FADE: Frequency-Aware Diffusion Model Factorization for Video Editing
authors: Yixuan Zhu, Haolin Wang, Shilin Ma, Wenliang Zhao, Yansong Tang, Lei Chen, Jie Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28426-28435
cities: ["Beijing", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_FADE_Frequency-Aware_Diffusion_Model_Factorization_for_Video_Editing_CVPR_2025_paper.html

abstract:
Recent advancements in diffusion frameworks have significantly enhanced video editing, achieving high fidelity and strong alignment with textual prompts. However, conventional approaches using image diffusion models fall short in handling video dynamics, particularly for challenging temporal edits like motion adjustments. While current video diffusion models produce high-quality results, adapting them for efficient editing remains difficult due to the heavy computational demands that prevent the direct application of previous image editing techniques. To overcome these limitations, we introduce FADE--a training-free yet highly effective video editing approach that fully leverages the inherent priors from pre-trained video diffusion models via frequency-aware factorization. Rather than simply using these models, we first analyze the attention patterns within the video model to reveal how video priors are distributed across different components. Building on these insights, we propose a factorization strategy to optimize each component's specialized role. Furthermore, we devise spectrum-guided modulation to refine the sampling trajectory with frequency domain cues, preventing information leakage and supporting efficient, versatile edits while preserving the basic spatial and temporal structure. Extensive experiments on real-world videos demonstrate that our method consistently delivers high-quality, realistic and temporally coherent editing results both qualitatively and quantitatively. Code is available at https://github.com/EternalEvan/FADE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_FADE_Frequency-Aware_Diffusion_Model_Factorization_for_Video_Editing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_FADE_Frequency-Aware_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Yixuan and Wang, Haolin and Ma, Shilin and Zhao, Wenliang and Tang, Yansong and Chen, Lei and Zhou, Jie},
    title     = {FADE: Frequency-Aware Diffusion Model Factorization for Video Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28426-28435}
}

raw_affiliation:
[{"affiliation": "Department of Automation, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Tsinghua Shenzhen International Graduate School, Tsinghua University", "city": "Shenzhen", "country": "China"}]

affiliations:
["Department of Automation, Tsinghua University", "Tsinghua Shenzhen International Graduate School, Tsinghua University"]


title: MotiF: Making Text Count in Image Animation with Motion Focal Loss
authors: Shijie Wang, Samaneh Azadi, Rohit Girdhar, Saketh Rambhatla, Chen Sun, Xi Yin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7773-7783
affiliations: ["Brown University", "GenAI, Meta"]
cities: ["Providence", "Menlo Park"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_MotiF_Making_Text_Count_in_Image_Animation_with_Motion_Focal_CVPR_2025_paper.html

abstract:
Text-Image-to-Video (TI2V) generation aims to generate a video from an image following a text description, which is also referred to as text-guided image animation. Most existing methods struggle to generate videos that align well with the text prompts, particularly when motion is specified. To overcome this limitation, we introduce MotiF, a simple yet effective approach that directs the model's learning to the regions with more motion, thereby improving the text alignment and motion generation. We use optical flow to generate a motion heatmap and weight the loss according to the intensity of the motion. This modified objective leads to noticeable improvements and complements existing methods that utilize motion priors as model inputs.  Additionally, due to the lack of a diverse benchmark for evaluating TI2V generation, we propose TI2V-Bench, a dataset consists of 320 image-text pairs for robust evaluation. We present a human evaluation protocol that asks the annotators to select an overall preference between two videos followed by their justifications. Through a comprehensive evaluation, MotiF outperforms nine open-sourced models, achieving an average preference of 72%. The TI2V-Bench and additional results are released in https://wang-sj16.github.io/motif/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_MotiF_Making_Text_Count_in_Image_Animation_with_Motion_Focal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_MotiF_Making_Text_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Shijie and Azadi, Samaneh and Girdhar, Rohit and Rambhatla, Saketh and Sun, Chen and Yin, Xi},
    title     = {MotiF: Making Text Count in Image Animation with Motion Focal Loss},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7773-7783}
}

raw_affiliation:
[{"affiliation": "Brown University", "city": "Providence", "country": "USA"}, {"affiliation": "GenAI, Meta", "city": "Menlo Park", "country": "USA"}]


authors: Longyu Yang, Ping Hu, Shangbo Yuan, Lu Zhang, Jun Liu, Hengtao Shen, Xiaofeng Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 139-149
affiliations: ["UESTC", "DLUT", "Lancaster University", "Tongji University"]
cities: ["Chengdu", "Dalian", "Lancaster", "Shanghai"]
countries: ["China", "China", "UK", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Towards_Explicit_Geometry-Reflectance_Collaboration_for_Generalized_LiDAR_Segmentation_in_Adverse_CVPR_2025_paper.html

title:
Towards Explicit Geometry-Reflectance Collaboration for Generalized LiDAR Segmentation in Adverse Weather

abstract:
Existing LiDAR semantic segmentation models often suffer from decreased accuracy when exposed to adverse weather conditions. Recent methods addressing this issue focus on enhancing training data through weather simulation or universal augmentation techniques. However, few works have studied the negative impacts caused by the heterogeneous domain shifts in the geometric structure and reflectance intensity of point clouds. In this paper, we delve into this challenge and address it with a novel Geometry-Reflectance Collaboration (GRC) framework that explicitly separates feature extraction for geometry and reflectance. Specifically, GRC employs a dual-branch architecture designed to process geometric and reflectance features independently initially, thereby capitalizing on their distinct characteristic. Then, GRC adopts a robust multi-level feature collaboration module to suppress redundant and unreliable information from both branches. Consequently, without complex simulation or augmentation, our method effectively extracts intrinsic information about the scene while suppressing interference, thus achieving better robustness and generalization in adverse weather conditions. We demonstrate the effectiveness of GRC through comprehensive experiments on challenging benchmarks, showing that our method outperforms previous approaches and establishes new state-of-the-art results.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Towards_Explicit_Geometry-Reflectance_Collaboration_for_Generalized_LiDAR_Segmentation_in_Adverse_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Longyu and Hu, Ping and Yuan, Shangbo and Zhang, Lu and Liu, Jun and Shen, Hengtao and Zhu, Xiaofeng},
    title     = {Towards Explicit Geometry-Reflectance Collaboration for Generalized LiDAR Segmentation in Adverse Weather},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {139-149}
}

raw_affiliation:
[{"affiliation": "UESTC", "city": "Chengdu", "country": "China"}, {"affiliation": "DLUT", "city": "Dalian", "country": "China"}, {"affiliation": "Lancaster University", "city": "Lancaster", "country": "UK"}, {"affiliation": "Tongji University", "city": "Shanghai", "country": "China"}]


title: Data Synthesis with Diverse Styles for Face Recognition via 3DMM-Guided Diffusion
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21203-21214
affiliations: ["Fudan University", "Youtu Lab, Tencent", "WeChat Pay Lab33, Tencent"]
cities: ["Shanghai", "Shenzhen", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mi_Data_Synthesis_with_Diverse_Styles_for_Face_Recognition_via_3DMM-Guided_CVPR_2025_paper.html

authors:
Yuxi Mi, Zhizhou Zhong, Yuge Huang, Qiuyang Yuan, Xuan Zhao, Jianqing Xu, Shouhong Ding, Shaoming Wang, Rizen Guo, Shuigeng Zhou

abstract:
Identity-preserving face synthesis aims to generate synthetic face images of virtual subjects that can substitute real-world data for training face recognition models. While prior arts strive to create images with consistent identities and diverse styles, they face a trade-off between them. Identifying their limitation of treating style variation as subject-agnostic and observing that real-world persons actually have distinct, subject-specific styles, this paper introduces MorphFace, a diffusion-based face generator. The generator learns fine-grained facial styles, e.g., shape, pose and expression, from the renderings of a 3D morphable model (3DMM). It also learns identities from an off-the-shelf recognition model. To create virtual faces, the generator is conditioned on novel identities of unlabeled synthetic faces, and novel styles that are statistically sampled from a real-world prior distribution. The sampling especially accounts for both intra-subject variation and subject distinctiveness. A context blending strategy is employed to enhance the generator's responsiveness to identity and style conditions. Extensive experiments show that MorphFace outperforms the best prior arts in face recognition efficacy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mi_Data_Synthesis_with_Diverse_Styles_for_Face_Recognition_via_3DMM-Guided_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mi_Data_Synthesis_with_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mi_2025_CVPR,
    author    = {Mi, Yuxi and Zhong, Zhizhou and Huang, Yuge and Yuan, Qiuyang and Zhao, Xuan and Xu, Jianqing and Ding, Shouhong and Wang, Shaoming and Guo, Rizen and Zhou, Shuigeng},
    title     = {Data Synthesis with Diverse Styles for Face Recognition via 3DMM-Guided Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21203-21214}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Youtu Lab, Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "WeChat Pay Lab33, Tencent", "city": "Shenzhen", "country": "China"}]


title: Diffusion Self-Distillation for Zero-Shot Customized Image Generation
authors: Shengqu Cai, Eric Ryan Chan, Yunzhi Zhang, Leonidas Guibas, Jiajun Wu, Gordon Wetzstein
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18434-18443
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/Cai_Diffusion_Self-Distillation_for_Zero-Shot_Customized_Image_Generation_CVPR_2025_paper.html

abstract:
Text-to-image diffusion models produce impressive results but are frustrating tools for artists who desire fine-grained control. For example, a common use case is to create images of a specific instance in novel contexts, i.e., "identity-preserving generation". This setting, along with many other tasks (e.g., relighting), is a natural fit for image+text-conditional generative models. However, there is insufficient high-quality paired data to train such a model directly. We propose Diffusion Self-Distillation, a method for using a pre-trained text-to-image model to generate its own dataset for text-conditioned image-to-image tasks. We first leverage a text-to-image diffusion model's in-context generation ability to create grids of images and curate a large paired dataset with the help of a Visual-Language Model. We then fine-tune the text-to-image model into a text+image-to-image model using the curated paired dataset. We demonstrate that Diffusion Self-Distillation outperforms existing zero-shot methods and is competitive with per-instance tuning techniques on a wide range of identity-preservation generation tasks, without requiring test-time optimization.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cai_Diffusion_Self-Distillation_for_Zero-Shot_Customized_Image_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cai_Diffusion_Self-Distillation_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cai_2025_CVPR,
    author    = {Cai, Shengqu and Chan, Eric Ryan and Zhang, Yunzhi and Guibas, Leonidas and Wu, Jiajun and Wetzstein, Gordon},
    title     = {Diffusion Self-Distillation for Zero-Shot Customized Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18434-18443}
}


title: Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model
authors: Leheng Zhang, Weiyi You, Kexuan Shi, Shuhang Gu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17980-17989
affiliations: ["University of Electronic Science and Technology of China"]
cities: ["Chengdu"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Uncertainty-guided_Perturbation_for_Image_Super-Resolution_Diffusion_Model_CVPR_2025_paper.html

abstract:
Diffusion-based image super-resolution methods have demonstrated significant advantages over GAN-based approaches, particularly in terms of perceptual quality. Building upon a lengthy Markov chain, diffusion-based methods possess remarkable modeling capacity, enabling them to achieve outstanding performance in real-world scenarios. Unlike previous methods that focus on modifying the noise schedule or sampling process to enhance performance, our approach emphasizes the improved utilization of LR information. We find that different regions of the LR image can be viewed as corresponding to different timesteps in a diffusion process, where flat areas are closer to the target HR distribution but edge and texture regions are farther away. In these flat areas, applying a slight noise is more advantageous for the reconstruction. We associate this characteristic with uncertainty and propose to apply uncertainty estimate to guide region-specific noise level control, a technique we refer to as Uncertainty-guided Noise Weighting. Pixels with lower uncertainty (i.e., flat regions) receive reduced noise to preserve more LR information, therefore improving performance. Furthermore, we modify the network architecture of previous methods to develop our Uncertainty-guided Perturbation Super-Resolution (UPSR) model. Extensive experimental results demonstrate that, despite reduced model size and training overhead, the proposed UPSR method outperforms current state-of-the-art methods across various datasets, both quantitatively and qualitatively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Uncertainty-guided_Perturbation_for_Image_Super-Resolution_Diffusion_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Uncertainty-guided_Perturbation_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Leheng and You, Weiyi and Shi, Kexuan and Gu, Shuhang},
    title     = {Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17980-17989}
}

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


title: Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning
authors: Yanbiao Ma, Wei Dai, Wenke Huang, Jiayi Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20958-20968
affiliations: ["Xidian University", "Xidian University", "Wuhan University", "Xidian University"]
cities: ["Xi'an", "Xi'an", "Wuhan", "Xi'an"]
countries: ["China", "China", "China", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Geometric_Knowledge-Guided_Localized_Global_Distribution_Alignment_for_Federated_Learning_CVPR_2025_paper.html

abstract:
Data heterogeneity in federated learning, characterized by a significant misalignment between local and global distributions, leads to divergent local optimization directions and hinders global model training. Existing studies mainly focus on optimizing local updates or global aggregation, but these indirect approaches demonstrate instability when handling highly heterogeneous data distributions, especially in scenarios where label skew and domain skew coexist. To address this, we propose a geometry-guided data generation method that centers on simulating the global embedding distribution locally. We first introduce the concept of the geometric shape of an embedding distribution and then address the challenge of obtaining global geometric shapes under privacy constraints. Subsequently, we propose GGEUR, which leverages global geometric shapes to guide the generation of new samples, enabling a closer approximation to the ideal global distribution. In single-domain scenarios, we augment samples based on global geometric shapes to enhance model generalization; in multi-domain scenarios, we further employ class prototypes to simulate the global distribution across domains. Extensive experimental results demonstrate that our method significantly enhances the performance of existing approaches in handling highly heterogeneous data, including scenarios with label skew, domain skew, and their coexistence.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_Geometric_Knowledge-Guided_Localized_Global_Distribution_Alignment_for_Federated_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_Geometric_Knowledge-Guided_Localized_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Yanbiao and Dai, Wei and Huang, Wenke and Chen, Jiayi},
    title     = {Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20958-20968}
}

raw_affiliation:
[{"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}]


title: Towards Human-Understandable Multi-Dimensional Concept Discovery
authors: Arne Grobrügge, Niklas Kühl, Gerhard Satzger, Philipp Spitzer
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20018-20027
affiliations: ["Karlsruhe Institute of Technology", "University of Bayreuth"]
cities: ["Karlsruhe", "Bayreuth"]
countries: ["Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Grobrugge_Towards_Human-Understandable_Multi-Dimensional_Concept_Discovery_CVPR_2025_paper.html

abstract:
Concept-based eXplainable AI (C-XAI) aims to overcome the limitations of traditional saliency maps by converting pixels into human-understandable concepts that are consistent across an entire dataset. A crucial aspect of C-XAI is completeness, which measures how well a set of concepts explains a model's decisions. Among C-XAI methods, Multi-Dimensional Concept Discovery (MCD) effectively improves completeness by breaking down the CNN latent space into distinct and interpretable concept subspaces. However, MCD's explanations can be difficult for humans to understand, raising concerns about their practical utility. To address this, we propose Human-Understandable Multi-dimensional Concept Discovery (HU-MCD). HU-MCD uses the Segment Anything Model for concept identification and implements a CNN-specific input masking technique to reduce noise introduced by traditional masking methods. These changes to MCD, paired with the completeness relation, enable HU-MCD to enhance concept understandability while maintaining explanation faithfulness. Our experiments, including human subject studies, show that HU-MCD provides more precise and reliable explanations than existing C-XAI methods. The code is available at https://github.com/grobruegge/hu-mcd.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Grobrugge_Towards_Human-Understandable_Multi-Dimensional_Concept_Discovery_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Grobrugge_Towards_Human-Understandable_Multi-Dimensional_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Grobrugge_2025_CVPR,
    author    = {Grobr\"ugge, Arne and K\"uhl, Niklas and Satzger, Gerhard and Spitzer, Philipp},
    title     = {Towards Human-Understandable Multi-Dimensional Concept Discovery},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20018-20027}
}

raw_affiliation:
[{"affiliation": "Karlsruhe Institute of Technology", "city": "Karlsruhe", "country": "Germany"}, {"affiliation": "University of Bayreuth", "city": "Bayreuth", "country": "Germany"}]


title: GlyphMastero: A Glyph Encoder for High-Fidelity Scene Text Editing
authors: Tong Wang, Ting Liu, Xiaochao Qu, Chengjing Wu, Luoqi Liu, Xiaolin Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28523-28532
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_GlyphMastero_A_Glyph_Encoder_for_High-Fidelity_Scene_Text_Editing_CVPR_2025_paper.html

abstract:
Scene text editing, a subfield of image editing, requires modifying texts in images while preserving style consistency and visual coherence with the surrounding environment. While diffusion-based methods have shown promise in text generation, they still struggle to produce high-quality results. These methods often generate distorted or unrecognizable characters, particularly when dealing with complex characters like Chinese. In such systems, characters are composed of intricate stroke patterns and spatial relationships that must be precisely maintained. We present GlyphMastero, a specialized glyph encoder designed to guide the latent diffusion model for generating texts with stroke-level precision. Our key insight is that existing methods, despite using pretrained OCR models for feature extraction, fail to capture the hierarchical nature of text structures - from individual strokes to stroke-level interactions to overall character-level structure. To address this, our glyph encoder explicitly models and captures the cross-level interactions between local-level individual characters and global-level text lines through our novel glyph attention module. Meanwhile, our model implements a feature pyramid network to fuse the multi-scale OCR backbone features at the global-level. Through these cross-level and multi-scale fusions, we obtain more detailed glyph-aware guidance, enabling precise control over the scene text generation process. Our method achieves an 18.02% improvement in sentence accuracy over the state-of-the-art multi-lingual scene text editing baseline, while simultaneously reducing the text-region Frechet inception distance by 53.28%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_GlyphMastero_A_Glyph_Encoder_for_High-Fidelity_Scene_Text_Editing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_GlyphMastero_A_Glyph_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Tong and Liu, Ting and Qu, Xiaochao and Wu, Chengjing and Liu, Luoqi and Hu, Xiaolin},
    title     = {GlyphMastero: A Glyph Encoder for High-Fidelity Scene Text Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28523-28532}
}

raw_affiliation:
[{"affiliation": "MT Lab, Meitu Inc.", "city": "Beijing", "country": "China"}, {"affiliation": "Department of Computer Science and Technology, BNRist, IDG/McGovern Institute for Brain Research, Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["MT Lab, Meitu Inc.", "Department of Computer Science and Technology, BNRist, IDG/McGovern Institute for Brain Research, Tsinghua University"]


title: ConText-CIR: Learning from Concepts in Text for Composed Image Retrieval
authors: Eric Xing, Pranavi Kolouju, Robert Pless, Abby Stylianou, Nathan Jacobs
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19638-19648
cities: ["St. Louis", "St. Louis", "Washington"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xing_ConText-CIR_Learning_from_Concepts_in_Text_for_Composed_Image_Retrieval_CVPR_2025_paper.html

abstract:
Composed image retrieval (CIR) is the task of retrieving a target image specified by a query image and a relative text that describes a semantic modification to the query image. Existing methods in CIR struggle to accurately represent the image and the text modification, resulting in subpar performance. To address this limitation, we introduce a CIR framework,  \name , trained with a Text Concept-Consistency loss that encourages the representations of noun phrases in the text modification to better attend to the relevant parts of the query image. To support training with this loss function, we also propose a synthetic data generation pipeline that creates training data from existing CIR datasets or unlabeled images. We show that these components together enable stronger performance on CIR tasks, setting a new state-of-the-art in composed image retrieval in both the supervised and zero-shot settings on multiple benchmark datasets, including CIRR and CIRCO. Source code, model checkpoints, and our new datasets are available at https://github.com/mvrl/ConText-CIR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xing_ConText-CIR_Learning_from_Concepts_in_Text_for_Composed_Image_Retrieval_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xing_ConText-CIR_Learning_from_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xing_2025_CVPR,
    author    = {Xing, Eric and Kolouju, Pranavi and Pless, Robert and Stylianou, Abby and Jacobs, Nathan},
    title     = {ConText-CIR: Learning from Concepts in Text for Composed Image Retrieval},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19638-19648}
}

raw_affiliation:
[{"affiliation": "Washington University in St. Louis", "city": "St. Louis", "country": "USA"}, {"affiliation": "Saint Louis University", "city": "St. Louis", "country": "USA"}, {"affiliation": "The George Washington University", "city": "Washington", "country": "USA"}]

affiliations:
["Washington University in St. Louis", "Saint Louis University", "The George Washington University"]


title: MaskGaussian: Adaptive 3D Gaussian Representation from Probabilistic Masks
authors: Yifei Liu, Zhihang Zhong, Yifan Zhan, Sheng Xu, Xiao Sun
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 681-690
affiliations: ["Shanghai AI Laboratory", "Beihang University", "The University of Tokyo"]
cities: ["Shanghai", "Beijing", "Tokyo"]
countries: ["China", "China", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_MaskGaussian_Adaptive_3D_Gaussian_Representation_from_Probabilistic_Masks_CVPR_2025_paper.html

abstract:
While 3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in novel view synthesis and real-time rendering, the high memory consumption due to the use of millions of Gaussians limits its practicality. To mitigate this issue, improvements have been made by pruning unnecessary Gaussians, either through a hand-crafted criterion or by using learned masks. However, these methods deterministically remove Gaussians based on a snapshot of the pruning moment, leading to sub-optimized reconstruction performance from a long-term perspective. To address this issue, we introduce MaskGaussian, which models Gaussians as probabilistic entities rather than permanently removing them, and utilize them according to their probability of existence. To achieve this, we propose a masked-rasterization technique that enables unused yet probabilistically existing Gaussians to receive gradients, allowing for dynamic assessment of their contribution to the evolving scene and adjustment of their probability of existence. Hence, the importance of Gaussians iteratively changes and the pruned Gaussians are selected diversely. Extensive experiments demonstrate the superiority of the proposed method in achieving better rendering quality with fewer Gaussians than previous pruning methods, pruning over 60% of Gaussians on average with only a 0.02 PSNR decline. Our code can be found at: https://github.com/kaikai23/MaskGaussian

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_MaskGaussian_Adaptive_3D_Gaussian_Representation_from_Probabilistic_Masks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_MaskGaussian_Adaptive_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yifei and Zhong, Zhihang and Zhan, Yifan and Xu, Sheng and Sun, Xiao},
    title     = {MaskGaussian: Adaptive 3D Gaussian Representation from Probabilistic Masks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {681-690}
}

raw_affiliation:
[{"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "The University of Tokyo", "city": "Tokyo", "country": "Japan"}]


title: Perturb-and-Revise: Flexible 3D Editing with Generative Trajectories
authors: Susung Hong, Johanna Karras, Ricardo Martin-Brualla, Ira Kemelmacher-Shlizerman
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16293-16303
affiliations: ["University of Washington", "Google Research"]
cities: ["Seattle", "Mountain View"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hong_Perturb-and-Revise_Flexible_3D_Editing_with_Generative_Trajectories_CVPR_2025_paper.html

abstract:
Recent advancements in text-based diffusion models have accelerated progress in 3D reconstruction and text-based 3D editing. Although existing 3D editing methods excel at modifying color, texture, and style, they struggle with extensive geometric or appearance changes, thus limiting their applications. To this end, we propose Perturb-and-Revise, which makes possible a variety of NeRF editing. First, we perturb the NeRF parameters with random initializations to create a versatile initialization. The level of perturbation is determined automatically through analysis of the local loss landscape. Then, we revise the edited NeRF via generative trajectories. Combined with the generative process, we impose identity-preserving gradients to refine the edited NeRF. Extensive experiments demonstrate that Perturb-and-Revise facilitates flexible, effective, and consistent editing of color, appearance, and geometry in 3D. For 360deg results, please visit our project page: https://susunghong.github.io/Perturb-and-Revise.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hong_Perturb-and-Revise_Flexible_3D_Editing_with_Generative_Trajectories_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hong_Perturb-and-Revise_Flexible_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hong_2025_CVPR,
    author    = {Hong, Susung and Karras, Johanna and Martin-Brualla, Ricardo and Kemelmacher-Shlizerman, Ira},
    title     = {Perturb-and-Revise: Flexible 3D Editing with Generative Trajectories},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16293-16303}
}

raw_affiliation:
[{"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "Google Research", "city": "Mountain View", "country": "USA"}]


title: Birth and Death of a Rose
authors: Chen Geng, Yunzhi Zhang, Shangzhe Wu, Jiajun Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26102-26113
raw_affiliation: [{"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}]
affiliations: ["Stanford University"]
cities: ["Stanford"]
countries: ["USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Geng_Birth_and_Death_of_a_Rose_CVPR_2025_paper.html

abstract:
We study the problem of generating temporal object intrinsics--temporally evolving sequences of object geometry, reflectance, and texture, such as a blooming rose--from pre-trained 2D foundation models. Unlike conventional 3D modeling and animation techniques that require extensive manual effort and expertise, we introduce a method that generates such assets with signals distilled from pretrained 2D diffusion models. To ensure the temporal consistency of object intrinsics, we propose Neural Templates for temporal-state-guided distillation, derived automatically from image features from self-supervised learning. Our method can generate high-quality temporal object intrinsics for several natural phenomena and enable the sampling and controllable rendering of these dynamic objects from any viewpoint, under any environmental lighting conditions, at any time of their lifespan. Project website: https://chen-geng.com/rose4d.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Geng_Birth_and_Death_of_a_Rose_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Geng_2025_CVPR,
    author    = {Geng, Chen and Zhang, Yunzhi and Wu, Shangzhe and Wu, Jiajun},
    title     = {Birth and Death of a Rose},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26102-26113}
}


title: Learning Compatible Multi-Prize Subnetworks for Asymmetric Retrieval
authors: Yushuai Sun, Zikun Zhou, Dongmei Jiang, Yaowei Wang, Jun Yu, Guangming Lu, Wenjie Pei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15255-15264
affiliations: ["Harbin Institute of Technology, Shenzhen", "Pengcheng Laboratory"]
cities: ["Shenzhen", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_Learning_Compatible_Multi-Prize_Subnetworks_for_Asymmetric_Retrieval_CVPR_2025_paper.html

abstract:
Asymmetric retrieval is a typical scenario in real-world retrieval systems, where compatible models of varying capacities are deployed on platforms with different resource configurations. Existing methods generally train pre-defined networks or subnetworks with capacities specifically designed for pre-determined platforms, using compatible learning. Nevertheless, these methods suffer from limited flexibility for multi-platform deployment. For example, when introducing a new platform into the retrieval systems, developers have to train an additional model at an appropriate capacity that is compatible with existing models via backward-compatible learning. In this paper, we propose a Prunable Network with self-compatibility, which allows developers to generate compatible subnetworks at any desired capacity through post-training pruning. Thus it allows the creation of a sparse subnetwork matching the resources of the new platform without additional training. Specifically, we optimize both the architecture and weight of subnetworks at different capacities within a dense network in compatible learning. We also design a conflict-aware gradient integration scheme to handle the gradient conflicts between the dense network and subnetworks during compatible learning. Extensive experiments on diverse benchmarks and visual backbones demonstrate the effectiveness of our method. The code will be made publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_Learning_Compatible_Multi-Prize_Subnetworks_for_Asymmetric_Retrieval_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_Learning_Compatible_Multi-Prize_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Yushuai and Zhou, Zikun and Jiang, Dongmei and Wang, Yaowei and Yu, Jun and Lu, Guangming and Pei, Wenjie},
    title     = {Learning Compatible Multi-Prize Subnetworks for Asymmetric Retrieval},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15255-15264}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of Technology, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}]


title: SoundVista: Novel-View Ambient Sound Synthesis via Visual-Acoustic Binding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8331-8341
affiliations: ["University of Washington", "Codec Avatars Lab, Pittsburgh, Meta", "Reality Labs Research, Meta"]
cities: ["Seattle", "Pittsburgh", "Menlo Park"]
countries: ["USA", "USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_SoundVista_Novel-View_Ambient_Sound_Synthesis_via_Visual-Acoustic_Binding_CVPR_2025_paper.html

authors:
Mingfei Chen, Israel D. Gebru, Ishwarya Ananthabhotla, Christian Richardt, Dejan Markovic, Jake Sandakly, Steven Krenn, Todd Keebler, Eli Shlizerman, Alexander Richard

abstract:
We introduce SoundVista, a method to generate the ambient sound of an arbitrary scene at novel viewpoints. Given a pre-acquired recording of the scene from sparsely distributed microphones, SoundVista can synthesize the sound of that scene from an unseen target viewpoint. The method learns the underlying acoustic transfer function that relates the signals acquired at the distributed microphones to the signal at the target viewpoint, using a limited number of known recordings. Unlike existing works, our method does not require constraints or prior knowledge of sound source details. Moreover, our method efficiently adapts to diverse room layouts, reference microphone configurations and unseen environments. To enable this, we introduce a visual-acoustic binding module that learns visual embeddings linked with local acoustic properties from panoramic RGB and depth data. We first leverage these embeddings to optimize the placement of reference microphones in any given scene. During synthesis, we leverage multiple embeddings extracted from reference locations to get adaptive weights for their contribution, conditioned on target viewpoint. We benchmark the task on both publicly available data and real-world settings. We demonstrate significant improvements over existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_SoundVista_Novel-View_Ambient_Sound_Synthesis_via_Visual-Acoustic_Binding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_SoundVista_Novel-View_Ambient_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Mingfei and Gebru, Israel D. and Ananthabhotla, Ishwarya and Richardt, Christian and Markovic, Dejan and Sandakly, Jake and Krenn, Steven and Keebler, Todd and Shlizerman, Eli and Richard, Alexander},
    title     = {SoundVista: Novel-View Ambient Sound Synthesis via Visual-Acoustic Binding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8331-8341}
}

raw_affiliation:
[{"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "Codec Avatars Lab, Pittsburgh, Meta", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "Reality Labs Research, Meta", "city": "Menlo Park", "country": "USA"}]


title: CLIP Under the Microscope: A Fine-Grained Analysis of Multi-Object Representation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9308-9317
raw_affiliation: [{"affiliation": "Sharif University of Technology", "city": "Tehran", "country": "Iran"}]
affiliations: ["Sharif University of Technology"]
cities: ["Tehran"]
countries: ["Iran"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Abbasi_CLIP_Under_the_Microscope_A_Fine-Grained_Analysis_of_Multi-Object_Representation_CVPR_2025_paper.html

authors:
Reza Abbasi, Ali Nazari, Aminreza Sefid, Mohammadali Banayeeanzade, Mohammad Hossein Rohban, Mahdieh Soleymani Baghshah

abstract:
Contrastive Language-Image Pre-training (CLIP) models excel in zero-shot classification, yet face challenges in complex multi-object scenarios. This study offers a comprehensive analysis of CLIP's limitations in these contexts using a specialized dataset, ComCO, designed to evaluate CLIP's encoders in diverse multi-object scenarios. Our findings reveal significant biases: the text encoder prioritizes first-mentioned objects, and the image encoder favors larger objects. Through retrieval and classification tasks, we quantify these biases across multiple CLIP variants and trace their origins to CLIP's training process, supported by analyses of the LAION dataset and training progression. Our image-text matching experiments show substantial performance drops when object size or token order changes, underscoring CLIP's instability with rephrased but semantically similar captions. Extending this to longer captions and text-to-image models like Stable Diffusion, we demonstrate how prompt order influences object prominence in generated images.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Abbasi_CLIP_Under_the_Microscope_A_Fine-Grained_Analysis_of_Multi-Object_Representation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Abbasi_CLIP_Under_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Abbasi_2025_CVPR,
    author    = {Abbasi, Reza and Nazari, Ali and Sefid, Aminreza and Banayeeanzade, Mohammadali and Rohban, Mohammad Hossein and Baghshah, Mahdieh Soleymani},
    title     = {CLIP Under the Microscope: A Fine-Grained Analysis of Multi-Object Representation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9308-9317}
}


authors: Shu Wang, Yanbo Gao, Shuai Li, Chong Lv, Xun Cai, Chuankun Li, Hui Yuan, Jinglin Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21381-21391
affiliations: ["Shandong University", "North University of China"]
cities: ["Jinan", "Taiyuan"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_MetricGrids_Arbitrary_Nonlinear_Approximation_with_Elementary_Metric_Grids_based_Implicit_CVPR_2025_paper.html

title:
MetricGrids: Arbitrary Nonlinear Approximation with Elementary Metric Grids based Implicit Neural Representation

abstract:
This paper presents MetricGrids, a novel grid-based neural representation that combines elementary metric grids in various metric spaces to approximate complex nonlinear signals. While grid-based representations are widely adopted for their efficiency and scalability, the existing feature grids with linear indexing for continuous-space points can only provide degenerate linear latent space representations, and such representations cannot be adequately compensated to represent complex nonlinear signals by the following compact decoder. To address this problem while keeping the simplicity of a regular grid structure, our approach builds upon the standard grid-based paradigm by constructing multiple elementary metric grids as high-order terms to approximate complex nonlinearities, following the Taylor expansion principle. Furthermore, we enhance model compactness with hash encoding based on different sparsities of the grids to prevent detrimental hash collisions, and a high-order extrapolation decoder to reduce explicit grid storage requirements. experimental results on both 2D and 3D reconstructions demonstrate the superior fitting and rendering accuracy of the proposed method across diverse signal types, validating its robustness and generalizability.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_MetricGrids_Arbitrary_Nonlinear_Approximation_with_Elementary_Metric_Grids_based_Implicit_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_MetricGrids_Arbitrary_Nonlinear_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Shu and Gao, Yanbo and Li, Shuai and Lv, Chong and Cai, Xun and Li, Chuankun and Yuan, Hui and Zhang, Jinglin},
    title     = {MetricGrids: Arbitrary Nonlinear Approximation with Elementary Metric Grids based Implicit Neural Representation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21381-21391}
}

raw_affiliation:
[{"affiliation": "Shandong University", "city": "Jinan", "country": "China"}, {"affiliation": "North University of China", "city": "Taiyuan", "country": "China"}]


title: Navigating Image Restoration with VAR's Distribution Alignment Prior
authors: Siyang Wang, Naishan Zheng, Jie Huang, Feng Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7559-7569
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Navigating_Image_Restoration_with_VARs_Distribution_Alignment_Prior_CVPR_2025_paper.html

abstract:
Generative models trained on extensive high-quality datasets effectively capture the structural and statistical properties of clean images, rendering them powerful priors for transforming degraded features into clean ones in image restoration. VAR, a novel image generative paradigm, surpasses diffusion models in generation quality by applying a next-scale prediction approach. It progressively captures both global structures and fine-grained details through the autoregressive process, consistent with the multi-scale restoration principle widely acknowledged in the restoration community. Furthermore, we observe that during the image reconstruction process utilizing VAR, scale predictions automatically modulate the input, facilitating the alignment of representations at subsequent scales with the distribution of clean images. To harness VAR's adaptive distribution alignment capability in image restoration tasks, we formulate the multi-scale latent representations within VAR as the restoration prior, thus advancing our delicately designed VarFormer framework. The strategic application of these priors enables our VarFormer to achieve remarkable generalization on unseen tasks while also reducing training computational costs. Extensive experiments underscores that our VarFormer outperforms existing multi-task image restoration methods across various restoration tasks. The code is available at https://github.com/siywang541/Varformer.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Navigating_Image_Restoration_with_VARs_Distribution_Alignment_Prior_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Navigating_Image_Restoration_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Siyang and Zheng, Naishan and Huang, Jie and Zhao, Feng},
    title     = {Navigating Image Restoration with VAR's Distribution Alignment Prior},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7559-7569}
}

raw_affiliation:
[{"affiliation": "MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China", "city": "Hefei", "country": "China"}]

affiliations:
["MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China"]


title: MovieBench: A Hierarchical Movie Level Dataset for Long Video Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28984-28994
affiliations: ["Show Lab, National University of Singapore", "Zhejiang University"]
cities: ["Singapore", "Hangzhou"]
countries: ["Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_MovieBench_A_Hierarchical_Movie_Level_Dataset_for_Long_Video_Generation_CVPR_2025_paper.html

authors:
Weijia Wu, Mingyu Liu, Zeyu Zhu, Xi Xia, Haoen Feng, Wen Wang, Kevin Qinghong Lin, Chunhua Shen, Mike Zheng Shou

abstract:
Recent advancements in video generation models, such as Stable Video Diffusion, have shown promising results, but these works primarily focus on short videos, often limited to a single scene and lacking a rich storyline. These models struggle with generating long videos that involve multiple scenes, coherent narratives, and consistent characters.  Furthermore, there is currently no publicly accessible dataset specifically designed for analyzing, evaluating, and training models for long video generation. In this paper, we present MovieBench: A Hierarchical Movie-Level Dataset for Long Video Generation,  which addresses these challenges by providing unique contributions:  (1) character consistency across scenes,  (2) long videos with rich and coherent storylines, and (3) multi-scene narratives. MovieBench features three distinct levels of annotation: the movie level, which provides a broad overview of the film; the scene level, offering a mid-level understanding of the narrative; and the shot level, which emphasizes specific moments with detailed descriptions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_MovieBench_A_Hierarchical_Movie_Level_Dataset_for_Long_Video_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_MovieBench_A_Hierarchical_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Weijia and Liu, Mingyu and Zhu, Zeyu and Xia, Xi and Feng, Haoen and Wang, Wen and Lin, Kevin Qinghong and Shen, Chunhua and Shou, Mike Zheng},
    title     = {MovieBench: A Hierarchical Movie Level Dataset for Long Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28984-28994}
}

raw_affiliation:
[{"affiliation": "Show Lab, National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]


title: Dissecting and Mitigating Diffusion Bias via Mechanistic Interpretability
authors: Yingdong Shi, Changming Li, Yifan Wang, Yongxiang Zhao, Anqi Pang, Sibei Yang, Jingyi Yu, Kan Ren
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8192-8202
affiliations: ["ShanghaiTech University", "Stony Brook University", "Tencent PCG"]
cities: ["Shanghai", "Stony Brook", "Shenzhen"]
countries: ["China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shi_Dissecting_and_Mitigating_Diffusion_Bias_via_Mechanistic_Interpretability_CVPR_2025_paper.html

abstract:
Diffusion models have demonstrated impressive capabilities in synthesizing diverse content. However, despite their high-quality outputs, these models often perpetuate social biases, including those related to gender and race. These biases can potentially contribute to harmful real-world consequences, reinforcing stereotypes and exacerbating inequalities in various social contexts. While existing research on diffusion bias mitigation has predominantly focused on guiding content generation, it often neglects the intrinsic mechanisms within diffusion models that causally drive biased outputs.  In this paper, we investigate the internal processes of diffusion models, identifying specific decision-making mechanisms, termed bias features, embedded within the model architecture. By directly manipulating these features, our method precisely isolates and adjusts the elements responsible for bias generation, permitting granular control over the bias levels in the generated content. Through experiments on both unconditional and conditional diffusion models across various social bias attributes, we demonstrate our method's efficacy in managing generation distribution while preserving image quality. We also dissect the discovered model mechanism, revealing different intrinsic features controlling fine-grained aspects of generation, boosting further research on mechanistic interpretability of diffusion models. The project website is at \href https://foundation-model-research.github.io/difflens  https://foundation-model-research.github.io/difflens .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shi_Dissecting_and_Mitigating_Diffusion_Bias_via_Mechanistic_Interpretability_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shi_Dissecting_and_Mitigating_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shi_2025_CVPR,
    author    = {Shi, Yingdong and Li, Changming and Wang, Yifan and Zhao, Yongxiang and Pang, Anqi and Yang, Sibei and Yu, Jingyi and Ren, Kan},
    title     = {Dissecting and Mitigating Diffusion Bias via Mechanistic Interpretability},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8192-8202}
}

raw_affiliation:
[{"affiliation": "ShanghaiTech University", "city": "Shanghai", "country": "China"}, {"affiliation": "Stony Brook University", "city": "Stony Brook", "country": "USA"}, {"affiliation": "Tencent PCG", "city": "Shenzhen", "country": "China"}]


authors: Manon Dampfhoffer, Thomas Mesquida, Damien Joubert, Thomas Dalgaty, Pascal Vivet, Christoph Posch
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6909-6918
affiliations: ["Univ. Grenoble Alpes, CEA, List, F-38000 Grenoble, France", "Prophesee, Paris, France"]
cities: ["Grenoble", "Paris"]
countries: ["France", "France"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dampfhoffer_Graph_Neural_Network_Combining_Event_Stream_and_Periodic_Aggregation_for_CVPR_2025_paper.html

title:
Graph Neural Network Combining Event Stream and Periodic Aggregation for Low-Latency Event-based Vision

abstract:
Event-based cameras asynchronously detect changes in light intensity with high temporal resolution, making them a promising alternative to RGB camera for low-latency and low-power optical flow estimation. However, state-of-the-art convolutional neural network methods create frames from the event stream, therefore losing the opportunity to exploit events for both sparse computations and low-latency prediction. On the other hand, asynchronous event graph methods could leverage both, but at the cost of avoiding any form of time accumulation, which limits the prediction accuracy. In this paper, we propose to break this accuracy-latency trade-off with a novel architecture combining an asynchronous accumulation-free event branch and a periodic aggregation branch. The periodic branch performs feature aggregations on the event graphs of past data to extract global context information, which improves accuracy without introducing any latency.The solution could predict optical flow per event with a latency of tens of microseconds on asynchronous hardware, which represents a gain of three orders of magnitude with respect to state-of-the-art frame-based methods, with 48x less operations per second. We show that the solution can detect rapid motion changes faster than a periodic output. This work proposes, for the first time, an effective solution for ultra low-latency and low-power optical flow prediction from event cameras.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dampfhoffer_Graph_Neural_Network_Combining_Event_Stream_and_Periodic_Aggregation_for_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Dampfhoffer_2025_CVPR,
    author    = {Dampfhoffer, Manon and Mesquida, Thomas and Joubert, Damien and Dalgaty, Thomas and Vivet, Pascal and Posch, Christoph},
    title     = {Graph Neural Network Combining Event Stream and Periodic Aggregation for Low-Latency Event-based Vision},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6909-6918}
}

raw_affiliation:
[{"affiliation": "Univ. Grenoble Alpes, CEA, List, F-38000 Grenoble, France", "city": "Grenoble", "country": "France"}, {"affiliation": "Prophesee, Paris, France", "city": "Paris", "country": "France"}]


title: Be More Specific: Evaluating Object-centric Realism in Synthetic Images
authors: Anqi Liang, Ciprian Corneanu, Qianli Feng, Giorgio Giannone, Aleix Martinez
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28842-28851
raw_affiliation: [{"affiliation": "Unknown", "city": "Unknown", "country": "Unknown"}]
affiliations: ["Unknown"]
cities: ["Unknown"]
countries: ["Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_Be_More_Specific_Evaluating_Object-centric_Realism_in_Synthetic_Images_CVPR_2025_paper.html

abstract:
Evaluation of synthetic images is important for both model development and selection. An ideal evaluation should be specific, accurate and aligned with human perception. This paper addresses the problem of evaluating realism of objects in synthetic images. Although methods has been proposed to evaluate holistic realism, there are no methods tailored towards object-centric realism evaluation. In this work, we define a new standard for assessing object-centric realism that follows a shape-texture breakdown and proposes the first object-centric realism evaluation dataset for synthetic images. The dataset contains images generated from state-of-the-art image generative models and is richly annotated at object level across a diverse set of object categories. We then design and train the OLIP model, a dedicated architecture that considerably outperforms any existing baseline on object-centric realism evaluation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_Be_More_Specific_Evaluating_Object-centric_Realism_in_Synthetic_Images_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_Be_More_Specific_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Anqi and Corneanu, Ciprian and Feng, Qianli and Giannone, Giorgio and Martinez, Aleix},
    title     = {Be More Specific: Evaluating Object-centric Realism in Synthetic Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28842-28851}
}


title: Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning
authors: Lei-Lei Ma, Shuo Xu, Ming-Kun Xie, Lei Wang, Dengdi Sun, Haifeng Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25434-25443
cities: ["Hefei", "Nanjing", "Nanjing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Correlative_and_Discriminative_Label_Grouping_for_Multi-Label_Visual_Prompt_Tuning_CVPR_2025_paper.html

abstract:
Modeling label correlations has always played a pivotal role in multi-label image classification (MLC), attracting significant attention from researchers. However, recent studies have overemphasized co-occurrence relationships among labels, which can lead to overfitting risk on this overemphasis, resulting in suboptimal models. To tackle this problem, we advocate for balancing correlative and discriminative relationships among labels to mitigate the risk of overfitting and enhance model performance. To this end, we propose the Multi-Label Visual Prompt Tuning framework, a novel and parameter-efficient method that groups classes into multiple class subsets according to label co-occurrence and mutual exclusivity relationships, and then models them respectively to balance the two relationships. In this work, since each group contains multiple classes, multiple prompt tokens are adopted within Vision Transformer (ViT) to capture the correlation or discriminative label relationship within each group, and effectively learn correlation or discriminative representations for class subsets. On the other hand, each group contains multiple group-aware visual representations that may correspond to multiple classes, and the mixture of experts (MoE) model can cleverly assign them from the group-aware to the label-aware, adaptively obtaining label-aware representation, which is more conducive to classification. Experiments on multiple benchmark datasets show that our proposed approach achieves competitive results and outperforms SOTA methods on multiple pre-trained models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_Correlative_and_Discriminative_Label_Grouping_for_Multi-Label_Visual_Prompt_Tuning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_Correlative_and_Discriminative_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Lei-Lei and Xu, Shuo and Xie, Ming-Kun and Wang, Lei and Sun, Dengdi and Zhao, Haifeng},
    title     = {Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25434-25443}
}

raw_affiliation:
[{"affiliation": "Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University", "city": "Hefei", "country": "China"}, {"affiliation": "Nanjing University of Aeronautics and Astronautics", "city": "Nanjing", "country": "China"}, {"affiliation": "Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}]

affiliations:
["Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University", "Nanjing University of Aeronautics and Astronautics", "Nanjing University of Science and Technology"]


title: LoRACLR: Contrastive Adaptation for Customization of Diffusion Models
authors: Enis Simsar, Thomas Hofmann, Federico Tombari, Pinar Yanardag
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13189-13198
affiliations: ["ETH Z\u00a8urich", "TU Munich", "Google", "Virginia Tech"]
cities: ["Zurich", "Munich", "Mountain View", "Blacksburg"]
countries: ["Switzerland", "Germany", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Simsar_LoRACLR_Contrastive_Adaptation_for_Customization_of_Diffusion_Models_CVPR_2025_paper.html

abstract:
Recent advances in text-to-image customization have enabled high-fidelity, context-rich generation of personalized images, allowing specific concepts to appear in a variety of scenarios. However, current methods struggle with combining multiple personalized models, often leading to attribute entanglement or requiring separate training to preserve concept distinctiveness. We present LoRACLR, a novel approach for multi-concept image generation that merges multiple LoRA models, each fine-tuned for a distinct concept, into a single, unified model without additional individual fine-tuning. LoRACLR uses a contrastive objective to align and merge the weight spaces of these models, ensuring compatibility while minimizing interference. By enforcing distinct yet cohesive representations for each concept, LoRACLR enables efficient, scalable model composition for high-quality, multi-concept image synthesis. Our results highlight the effectiveness of LoRACLR in accurately merging multiple concepts, advancing the capabilities of personalized image generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Simsar_LoRACLR_Contrastive_Adaptation_for_Customization_of_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Simsar_LoRACLR_Contrastive_Adaptation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Simsar_2025_CVPR,
    author    = {Simsar, Enis and Hofmann, Thomas and Tombari, Federico and Yanardag, Pinar},
    title     = {LoRACLR: Contrastive Adaptation for Customization of Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13189-13198}
}

raw_affiliation:
[{"affiliation": "ETH Z\u00a8urich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "TU Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Google", "city": "Mountain View", "country": "USA"}, {"affiliation": "Virginia Tech", "city": "Blacksburg", "country": "USA"}]


title: ArtFormer: Controllable Generation of Diverse 3D Articulated Objects
authors: Jiayi Su, Youhe Feng, Zheng Li, Jinhua Song, Yangfan He, Botao Ren, Botian Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1894-1904
cities: ["Sepang", "Beijing", "Beijing", "Shenzhen", "Minneapolis", "Zhengzhou"]
countries: ["Malaysia", "China", "China", "China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Su_ArtFormer_Controllable_Generation_of_Diverse_3D_Articulated_Objects_CVPR_2025_paper.html

abstract:
This paper presents a novel framework for modeling and conditional generation of 3D articulated objects. Troubled by flexibility-quality tradeoffs, existing methods are often limited to using predefined structures or retrieving shapes from static datasets. To address these challenges, we parameterize an articulated object as a tree of tokens and employ a transformer to generate both the object's high-level geometry code and its kinematic relations. Subsequently, each sub-part's geometry is further decoded using a signed-distance-function (SDF) shape prior, facilitating the synthesis of high-quality 3D shapes. Our approach enables the generation of diverse objects with high-quality geometry and varying number of parts. Comprehensive experiments on conditional generation from text descriptions demonstrate the effectiveness and flexibility of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Su_ArtFormer_Controllable_Generation_of_Diverse_3D_Articulated_Objects_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Su_ArtFormer_Controllable_Generation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Su_2025_CVPR,
    author    = {Su, Jiayi and Feng, Youhe and Li, Zheng and Song, Jinhua and He, Yangfan and Ren, Botao and Xu, Botian},
    title     = {ArtFormer: Controllable Generation of Diverse 3D Articulated Objects},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1894-1904}
}

raw_affiliation:
[{"affiliation": "Xiamen University Malaysia", "city": "Sepang", "country": "Malaysia"}, {"affiliation": "Renmin University of China", "city": "Beijing", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Southern University of Science and Technology", "city": "Shenzhen", "country": "China"}, {"affiliation": "University of Minnesota-Twin Cities", "city": "Minneapolis", "country": "USA"}, {"affiliation": "Henan RunTai Digital Technology Group Co., Ltd.", "city": "Zhengzhou", "country": "China"}]

affiliations:
["Xiamen University Malaysia", "Renmin University of China", "Tsinghua University", "Southern University of Science and Technology", "University of Minnesota-Twin Cities", "Henan RunTai Digital Technology Group Co., Ltd."]


title: Opportunistic Single-Photon Time of Flight
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15852-15862
affiliations: ["Dept. of Computer Science, University of T oronto", "V ector Institute"]
cities: ["Toronto", "Toronto"]
countries: ["Canada", "Canada"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nousias_Opportunistic_Single-Photon_Time_of_Flight_CVPR_2025_paper.html

authors:
Sotiris Nousias, Mian Wei, Howard Xiao, Maxx Wu, Shahmeer Athar, Kevin J. Wang, Anagh Malik, David A. Barmherzig, David B. Lindell, Kyros N. Kutulakos

abstract:
Scattered light from pulsed lasers is increasingly part of our ambient illumination, as many devices rely on them for active 3D sensing. In this work, we ask: can these "ambient" light signals be detected and leveraged for passive 3D vision? We show that pulsed lasers, despite being weak and fluctuating at MHz to GHz frequencies, leave a distinctive sinc comb pattern in the temporal frequency domain of incident flux that is specific to each laser and invariant to the scene. This enables their passive detection and analysis with a free-running SPAD camera, even when they are unknown, asynchronous, out of sight, and emitting concurrently. We show how to synchronize with such lasers computationally, characterize their pulse emissions, separate their contributions, and--if many are present--localize them in 3D and recover a depth map of the camera's field of view. We use our camera prototype to demonstrate (1) a first-of-its-kind visualization of asynchronously propagating light pulses from multiple lasers through the same scene, (2) passive estimation of a laser's MHz-scale pulse repetition frequency with mHz precision, and (3) mm-scale 3D imaging over room-scale distances by passively harvesting photons from two or more out-of-view lasers.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nousias_Opportunistic_Single-Photon_Time_of_Flight_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nousias_Opportunistic_Single-Photon_Time_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Nousias_2025_CVPR,
    author    = {Nousias, Sotiris and Wei, Mian and Xiao, Howard and Wu, Maxx and Athar, Shahmeer and Wang, Kevin J. and Malik, Anagh and Barmherzig, David A. and Lindell, David B. and Kutulakos, Kyros N.},
    title     = {Opportunistic Single-Photon Time of Flight},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15852-15862}
}

raw_affiliation:
[{"affiliation": "Dept. of Computer Science, University of T oronto", "city": "Toronto", "country": "Canada"}, {"affiliation": "V ector Institute", "city": "Toronto", "country": "Canada"}]


title: Bridging Gait Recognition and Large Language Models Sequence Modeling
authors: Shaopeng Yang, Jilong Wang, Saihui Hou, Xu Liu, Chunshui Cao, Liang Wang, Yongzhen Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3460-3469
cities: ["Beijing", "Hefei", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Bridging_Gait_Recognition_and_Large_Language_Models_Sequence_Modeling_CVPR_2025_paper.html

abstract:
Gait sequences exhibit sequential structures and contextual relationships similar to those in natural language, where each element--whether a word or a gait step--is connected to its predecessors and successors. This similarity enables the transformation of gait sequences into "texts" containing identity-related information. Large Language Models (LLMs), designed to understand and generate sequential data, can thus be utilized for gait sequence modeling to enhance gait recognition performance. Leveraging these insights, we make a pioneering effort to apply LLMs to gait recognition, which we refer to as GaitLLM. Specifically, we propose the Gait-to-Language (G2L) module, which converts gait sequences into a textual format suitable for LLMs, and the Language-to-Gait (L2G) module, which maps the LLM's output back to the gait feature space, thereby bridging the gap between LLM outputs and gait recognition. Notably, GaitLLM leverages the powerful modeling capabilities of LLMs without relying on complex architectural designs, improving gait recognition performance with only a small number of trainable parameters. Our method achieves state-of-the-art results on four popular gait datasets--SUSTech1K, CCPG, Gait3D, and GREW--demonstrating the effectiveness of applying LLMs in this domain. This work highlights the potential of LLMs to significantly enhance gait recognition, paving the way for future research and practical applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Bridging_Gait_Recognition_and_Large_Language_Models_Sequence_Modeling_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Shaopeng and Wang, Jilong and Hou, Saihui and Liu, Xu and Cao, Chunshui and Wang, Liang and Huang, Yongzhen},
    title     = {Bridging Gait Recognition and Large Language Models Sequence Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3460-3469}
}

raw_affiliation:
[{"affiliation": "Beijing Normal University", "city": "Beijing", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "W ATRIX.AI", "city": "Beijing", "country": "China"}, {"affiliation": "Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["Beijing Normal University", "University of Science and Technology of China", "W ATRIX.AI", "Institute of Automation, Chinese Academy of Sciences"]


title: Argus: Vision-Centric Reasoning with Grounded Chain-of-Thought
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14268-14280
affiliations: ["University of Illinois Urbana-Champaign", "NVIDIA"]
cities: ["Urbana-Champaign", "Santa Clara"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Man_Argus_Vision-Centric_Reasoning_with_Grounded_Chain-of-Thought_CVPR_2025_paper.html

authors:
Yunze Man, De-An Huang, Guilin Liu, Shiwei Sheng, Shilong Liu, Liang-Yan Gui, Jan Kautz, Yu-Xiong Wang, Zhiding Yu

abstract:
Recent advances in multimodal large language models (MLLMs) have demonstrated remarkable capabilities in vision-language tasks, yet they often struggle with vision-centric scenarios where precise visual focus is needed for accurate reasoning. In this paper, we introduce Argus to address these limitations with a new visual attention grounding mechanism. Our approach employs object-centric grounding as visual chain-of-thought signals, enabling more effective goal-conditioned visual attention during multimodal reasoning tasks. Evaluations on diverse benchmarks demonstrate that Argus excels in both multimodal reasoning tasks and referring object grounding tasks. Extensive analysis further validates various design choices of Argus, and reveals the effectiveness of explicit language-guided visual region-of-interest engagement in MLLMs, highlighting the importance of advancing multimodal intelligence from a visual-centric perspective.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Man_Argus_Vision-Centric_Reasoning_with_Grounded_Chain-of-Thought_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Man_Argus_Vision-Centric_Reasoning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Man_2025_CVPR,
    author    = {Man, Yunze and Huang, De-An and Liu, Guilin and Sheng, Shiwei and Liu, Shilong and Gui, Liang-Yan and Kautz, Jan and Wang, Yu-Xiong and Yu, Zhiding},
    title     = {Argus: Vision-Centric Reasoning with Grounded Chain-of-Thought},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14268-14280}
}

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


authors: Jungin Park, Jiyoung Lee, Kwanghoon Sohn
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13661-13670
cities: ["Seoul", "Seoul", "Seoul", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Park_Bootstrap_Your_Own_Views_Masked_Ego-Exo_Modeling_for_Fine-grained_View-invariant_CVPR_2025_paper.html

title:
Bootstrap Your Own Views: Masked Ego-Exo Modeling for Fine-grained View-invariant Video Representations

abstract:
View-invariant representation learning from egocentric (first-person, ego) and exocentric (third-person, exo) videos is a promising approach toward generalizing video understanding systems across multiple viewpoints. However, this area has been underexplored due to the substantial differences in perspective, motion patterns, and context between ego and exo views. In this paper, we propose a novel masked ego-exo modeling that promotes both causal temporal dynamics and cross-view alignment, called Bootstrap Your Own Views (BYOV), for fine-grained view-invariant video representation learning from unpaired ego-exo videos. We highlight the importance of capturing the compositional nature of human actions as a basis for robust cross-view understanding. Specifically, self-view masking and cross-view masking predictions are designed to learn view-invariant and powerful representations concurrently. Experimental results demonstrate that our BYOV significantly surpasses existing approaches with notable gains across all metrics in four downstream ego-exo video tasks. The code is available at https://github.com/park-jungin/byov

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Park_Bootstrap_Your_Own_Views_Masked_Ego-Exo_Modeling_for_Fine-grained_View-invariant_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_Bootstrap_Your_Own_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Park_2025_CVPR,
    author    = {Park, Jungin and Lee, Jiyoung and Sohn, Kwanghoon},
    title     = {Bootstrap Your Own Views: Masked Ego-Exo Modeling for Fine-grained View-invariant Video Representations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13661-13670}
}

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

affiliations:
["Yonsei University", "Ewha Womans University", "NA VER AI Lab", "Korea Institute of Science and Technology (KIST)"]


authors: Daisheng Jin, Jiangbei Hu, Baixin Xu, Yuxin Dai, Chen Qian, Ying He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26409-26419
cities: ["Singapore", "Dalian", "Hong Kong"]
countries: ["Singapore", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jin_SFDM_Robust_Decomposition_of_Geometry_and_Reflectance_for_Realistic_Face_CVPR_2025_paper.html

title:
SFDM: Robust Decomposition of Geometry and Reflectance for Realistic Face Rendering from Sparse-view Images

abstract:
In this study, we introduce a novel two-stage technique for decomposing and reconstructing facial features from sparse-view images, a task made challenging by the unique geometry and complex skin reflectance of each individual. To synthesize 3D facial models more realistically, we endeavor to decouple key facial attributes from the RGB color, including geometry, diffuse reflectance, and specular reflectance. Specifically, we design a Sparse-view Face Decomposition Model (SFDM): 1) In the first stage, we create a general facial template from a wide array of individual faces, encapsulating essential geometric and reflectance characteristics. 2) Guided by this template, we refine a specific facial model for each individual in the second stage, considering the interaction between geometry and reflectance, as well as the effects of subsurface scattering on the skin. With these advances, our method can reconstruct high-quality facial representations from as few as three images. The comprehensive evaluation and comparison reveal that our approach outperforms existing methods by effectively disentangling geometric and reflectance components, significantly enhancing the quality of synthesized novel views, and paving the way for applications in facial relighting and reflectance editing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jin_SFDM_Robust_Decomposition_of_Geometry_and_Reflectance_for_Realistic_Face_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jin_SFDM_Robust_Decomposition_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Jin_2025_CVPR,
    author    = {Jin, Daisheng and Hu, Jiangbei and Xu, Baixin and Dai, Yuxin and Qian, Chen and He, Ying},
    title     = {SFDM: Robust Decomposition of Geometry and Reflectance for Realistic Face Rendering from Sparse-view Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26409-26419}
}

raw_affiliation:
[{"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "SenseTime Research", "city": "Hong Kong", "country": "China"}]

affiliations:
["S-Lab, Nanyang Technological University", "Dalian University of Technology", "SenseTime Research"]


title: DiSRT-In-Bed: Diffusion-Based Sim-to-Real Transfer Framework for In-Bed Human Mesh Recovery
authors: Jing Gao, Ce Zheng, Laszlo A. Jeni, Zackory Erickson
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1829-1838
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/Gao_DiSRT-In-Bed_Diffusion-Based_Sim-to-Real_Transfer_Framework_for_In-Bed_Human_Mesh_Recovery_CVPR_2025_paper.html

abstract:
In-bed human mesh recovery can be crucial and enabling for several healthcare applications, including sleep pattern monitoring, rehabilitation support, and pressure ulcer prevention. However, it is difficult to collect large real-world visual datasets in this domain, in part due to privacy and expense constraints, which in turn presents significant challenges for training and deploying deep learning models. Existing in-bed human mesh estimation methods often rely heavily on real-world data, limiting their ability to generalize across different in-bed scenarios, such as varying coverings and environmental settings. To address this, we propose a Sim-to-Real Transfer Framework for in-bed human mesh recovery from overhead depth images, which leverages large-scale synthetic data alongside limited or no real-world samples. We introduce a diffusion model that bridges the gap between synthetic data and real data to support generalization in real-world in-bed pose and body inference scenarios. Extensive experiments and ablation studies validate the effectiveness of our framework, demonstrating significant improvements in robustness and adaptability across diverse healthcare scenarios. Project page can be found at https://jing-g2.github.io/DiSRT-In-Bed/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_DiSRT-In-Bed_Diffusion-Based_Sim-to-Real_Transfer_Framework_for_In-Bed_Human_Mesh_Recovery_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_DiSRT-In-Bed_Diffusion-Based_Sim-to-Real_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Jing and Zheng, Ce and Jeni, Laszlo A. and Erickson, Zackory},
    title     = {DiSRT-In-Bed: Diffusion-Based Sim-to-Real Transfer Framework for In-Bed Human Mesh Recovery},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1829-1838}
}


title: Ouroboros3D: Image-to-3D Generation via 3D-aware Recursive Diffusion
authors: Hao Wen, Zehuan Huang, Yaohui Wang, Xinyuan Chen, Lu Sheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21631-21641
affiliations: ["School of Software, Beihang University", "Shanghai AI Laboratory", "V AST"]
cities: ["Beijing", "Shanghai", "Unknown"]
countries: ["China", "China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wen_Ouroboros3D_Image-to-3D_Generation_via_3D-aware_Recursive_Diffusion_CVPR_2025_paper.html

abstract:
Existing image-to-3D creation methods typically split the task into two individual stage: multi-view image generation and 3D reconstruction, leading to two main limitations: (1) In multi-view generation stage, the multi-view generated images present a challenge to preserving 3D consistency;; (2) In 3D reconstruction stage, there is a domain gap between real training data and generated multi-view input during inference. To address these issues, we propose Ouroboros3D, end-to-end trainable framework that integrates multi-view generation and 3D reconstruction into a  recursive diffusion process through feedback mechanism.Our framework operates through iterative cycles where each cycle consists of a denoising process and a reconstruction step.By incorporating a 3D-aware feedback mechanism, our multi-view generative model leverages the explicit 3D geometric information (e.g. texture, position) from the feedback of reconstruction results of the previous process as conditions, thus modeling consistency at the 3D geometric level. Furthermore, through joint training of both the multi-view generative and reconstruction models, we alleviate reconstruction stage domain gap and enable mutual enhancement within the recursive process. Experimental results demonstrate that Ouroboros3D outperforms methods that treat these stages separately and those that combine them only during inference, achieving superior multi-view consistency and producing 3D models with higher geometric realism.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wen_Ouroboros3D_Image-to-3D_Generation_via_3D-aware_Recursive_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wen_Ouroboros3D_Image-to-3D_Generation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wen_2025_CVPR,
    author    = {Wen, Hao and Huang, Zehuan and Wang, Yaohui and Chen, Xinyuan and Sheng, Lu},
    title     = {Ouroboros3D: Image-to-3D Generation via 3D-aware Recursive Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21631-21641}
}

raw_affiliation:
[{"affiliation": "School of Software, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "V AST", "city": "Unknown", "country": "Unknown"}]


title: QMambaBSR: Burst Image Super-Resolution with Query State Space Model
authors: Xin Di, Long Peng, Peizhe Xia, Wenbo Li, Renjing Pei, Yang Cao, Yang Wang, Zheng-Jun Zha
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23080-23090
cities: ["Hefei", "Shenzhen", "Xi'an"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Di_QMambaBSR_Burst_Image_Super-Resolution_with_Query_State_Space_Model_CVPR_2025_paper.html

abstract:
Burst super-resolution (BurstSR) aims to reconstruct high-resolution images by fusing subpixel details from multiple low-resolution burst frames. The primary challenge lies in effectively extracting useful information while mitigating the impact of high-frequency noise. Most existing methods rely on frame-by-frame fusion, which often struggles to distinguish informative subpixels from noise, leading to suboptimal performance. To address these limitations, we introduce a novel Query Mamba Burst Super-Resolution (QMambaBSR) network. Specifically, we observe that sub-pixels have consistent spatial distribution while noise appears randomly. Considering the entire burst sequence during fusion allows for more reliable extraction of consistent subpixels and better suppression of noise outliers. Based on this, a Query State Space Model (QSSM) is proposed for both inter-frame querying and intra-frame scanning, enabling a more efficient fusion of useful subpixels. Additionally, to overcome the limitations of static upsampling methods that often result in over-smoothing, we propose an Adaptive Upsampling (AdaUp) module that dynamically adjusts the upsampling kernel to suit the characteristics of different burst scenes, achieving superior detail reconstruction. Extensive experiments on four benchmark datasets--spanning both synthetic and real-world images--demonstrate that QMambaBSR outperforms existing state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Di_QMambaBSR_Burst_Image_Super-Resolution_with_Query_State_Space_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Di_QMambaBSR_Burst_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Di_2025_CVPR,
    author    = {Di, Xin and Peng, Long and Xia, Peizhe and Li, Wenbo and Pei, Renjing and Cao, Yang and Wang, Yang and Zha, Zheng-Jun},
    title     = {QMambaBSR: Burst Image Super-Resolution with Query State Space Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23080-23090}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Huawei Noah\u2019s Ark Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "Chang\u2019an University", "city": "Xi'an", "country": "China"}]

affiliations:
["University of Science and Technology of China", "Huawei Noah\u2019s Ark Lab", "Chang\u2019an University"]


title: Encapsulated Composition of Text-to-Image and Text-to-Video Models for High-Quality Video Synthesis
authors: Tongtong Su, Chengyu Wang, Bingyan Liu, Jun Huang, Dongming Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18209-18218
affiliations: ["Zhejiang University", "Alibaba Cloud Computing", "South China University of Technology"]
cities: ["Hangzhou", "Hangzhou", "Guangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Su_Encapsulated_Composition_of_Text-to-Image_and_Text-to-Video_Models_for_High-Quality_Video_CVPR_2025_paper.html

abstract:
In recent years, large text-to-video (T2V) synthesis models have garnered considerable attention for their abilities to generate videos from textual descriptions. However, achieving both high imaging quality and effective motion representation remains a significant challenge for these T2V models. Existing approaches often adapt pre-trained text-to-image (T2I) models to refine video frames, leading to issues such as flickering and artifacts due to inconsistencies across frames. In this paper, we introduce EVS, a training-free \underline E ncapsulated \underline V ideo \underline S ynthesizer that composes T2I and T2V models to enhance both visual fidelity and motion smoothness of generated videos. Our approach utilizes a well-trained diffusion-based T2I model to refine low-quality video frames by treating them as out-of-distribution samples, effectively optimizing them with noising and denoising steps. Meanwhile, we employ T2V backbones to ensure consistent motion dynamics. By encapsulating the T2V temporal-only prior into the T2I generation process, EVS successfully leverages the strengths of both types of models, resulting in videos of improved imaging and motion quality. Experimental results validate the effectiveness of our approach compared to previous approaches.Our composition process also leads to a significant improvement of 1.6x-4.5x speedup in inference time.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Su_Encapsulated_Composition_of_Text-to-Image_and_Text-to-Video_Models_for_High-Quality_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Su_Encapsulated_Composition_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Su_2025_CVPR,
    author    = {Su, Tongtong and Wang, Chengyu and Liu, Bingyan and Huang, Jun and Lu, Dongming},
    title     = {Encapsulated Composition of Text-to-Image and Text-to-Video Models for High-Quality Video Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18209-18218}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Alibaba Cloud Computing", "city": "Hangzhou", "country": "China"}, {"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}]


title: Multi-Group Proportional Representations for Text-to-Image Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23744-23754
cities: ["Seoul", "Cambridge", "Seoul"]
countries: ["South Korea", "USA", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jung_Multi-Group_Proportional_Representations_for_Text-to-Image_Models_CVPR_2025_paper.html

authors:
Sangwon Jung, Alex Oesterling, Claudio Mayrink Verdun, Sajani Vithana, Taesup Moon, Flavio P. Calmon

abstract:
Text-to-image (T2I) generative models can create vivid, realistic images from textual descriptions. As these models proliferate, they expose new concerns about their ability to represent diverse demographic groups, propagate stereotypes, and efface minority populations. Despite growing attention to the "safe" and "responsible" design of artificial intelligence (AI), there is no established methodology to systematically measure and control representational harms in image generation. This paper introduces a novel framework to measure the representation of intersectional groups in images generated by T2I models by applying the Multi-Group Proportional Representation (MPR) metric. MPR evaluates the worst-case deviation of representation statistics across given population groups in images produced by a generative model, allowing for flexible and context-specific measurements based on user requirements. We also develop an algorithm to optimize T2I models for this metric. Through experiments, we demonstrate that MPR can effectively measure representation statistics across multiple intersectional groups and, when used as a training objective, can guide models toward a more balanced generation across demographic groups while maintaining generation quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jung_Multi-Group_Proportional_Representations_for_Text-to-Image_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jung_Multi-Group_Proportional_Representations_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jung_2025_CVPR,
    author    = {Jung, Sangwon and Oesterling, Alex and Verdun, Claudio Mayrink and Vithana, Sajani and Moon, Taesup and Calmon, Flavio P.},
    title     = {Multi-Group Proportional Representations for Text-to-Image Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23744-23754}
}

raw_affiliation:
[{"affiliation": "ECE, Seoul National University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Harvard University", "city": "Cambridge", "country": "USA"}, {"affiliation": "ASRI/INMC/IPAI/AIIS, Seoul National University", "city": "Seoul", "country": "South Korea"}]

affiliations:
["ECE, Seoul National University", "Harvard University", "ASRI/INMC/IPAI/AIIS, Seoul National University"]


authors: Kaouther Messaoud, Matthieu Cord, Alexandre Alahi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27564-27574
affiliations: ["EPFL, Switzerland", "Valeo.ai, France", "EPFL, Switzerland"]
cities: ["Lausanne", "Paris", "Lausanne"]
countries: ["Switzerland", "France", "Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Messaoud_Towards_Generalizable_Trajectory_Prediction_using_Dual-Level_Representation_Learning_and_Adaptive_CVPR_2025_paper.html

title:
Towards Generalizable Trajectory Prediction using Dual-Level Representation Learning and Adaptive Prompting

abstract:
Existing vehicle trajectory prediction models struggle with generalizability, prediction uncertainties, and handling complex interactions. It is often due to limitations like complex architectures customized for a specific dataset and inefficient multimodal handling. We propose Perceiver with Register queries (PerReg+), a novel trajectory prediction framework that introduces: (1) Dual-Level Representation Learning via Self-Distillation (SD) and Masked Reconstruction (MR), capturing global context and fine-grained details. Additionally, our approach of reconstructing segment-level trajectories and lane segments from masked inputs with query drop, enables effective use of contextual information and improves generalization; (2) Enhanced Multimodality using register-based queries and pretraining, eliminating the need for clustering and suppression; and (3) Adaptive Prompt Tuning during fine-tuning, freezing the main architecture and optimizing a small number of prompts for efficient adaptation. PerReg+ sets a new state-of-the-art performance on nuScenes, Argoverse 2, and Waymo Open Motion Dataset (WOMD). Remarkable, our pretrained model reduces the error by 6.8% on smaller datasets, and multi-dataset training enhances generalization. In cross-domain tests, PerReg+ reduces B-FDE by 11.8% compared to its non-pretrained variant.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Messaoud_Towards_Generalizable_Trajectory_Prediction_using_Dual-Level_Representation_Learning_and_Adaptive_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Messaoud_Towards_Generalizable_Trajectory_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Messaoud_2025_CVPR,
    author    = {Messaoud, Kaouther and Cord, Matthieu and Alahi, Alexandre},
    title     = {Towards Generalizable Trajectory Prediction using Dual-Level Representation Learning and Adaptive Prompting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27564-27574}
}

raw_affiliation:
[{"affiliation": "EPFL, Switzerland", "city": "Lausanne", "country": "Switzerland"}, {"affiliation": "Valeo.ai, France", "city": "Paris", "country": "France"}, {"affiliation": "EPFL, Switzerland", "city": "Lausanne", "country": "Switzerland"}]


title: CoMatcher: Multi-View Collaborative Feature Matching
authors: Jintao Zhang, Zimin Xia, Mingyue Dong, Shuhan Shen, Linwei Yue, Xianwei Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21970-21980
cities: ["Wuhan", "Lausanne", "Beijing", "Wuhan"]
countries: ["China", "Switzerland", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_CoMatcher_Multi-View_Collaborative_Feature_Matching_CVPR_2025_paper.html

abstract:
This paper proposes a multi-view collaborative matching strategy for reliable track construction in complex scenarios. We observe that the pairwise matching paradigms applied to image set matching often result in ambiguous estimation when the selected independent pairs exhibit significant occlusions or extreme viewpoint changes. This challenge primarily stems from the inherent uncertainty in interpreting intricate 3D structures based on limited two-view observations, as the 3D-to-2D projection leads to significant information loss. To address this, we introduce CoMatcher, a deep multi-view matcher to (i) leverage complementary context cues from different views to form a holistic 3D scene understanding and (ii) utilize cross-view projection consistency to infer a reliable global solution. Building on CoMatcher, we develop a groupwise framework that fully exploits cross-view relationships for large-scale matching tasks. Extensive experiments on various complex scenarios demonstrate the superiority of our method over the mainstream two-view matching paradigm.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_CoMatcher_Multi-View_Collaborative_Feature_Matching_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_CoMatcher_Multi-View_Collaborative_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Jintao and Xia, Zimin and Dong, Mingyue and Shen, Shuhan and Yue, Linwei and Zheng, Xianwei},
    title     = {CoMatcher: Multi-View Collaborative Feature Matching},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21970-21980}
}

raw_affiliation:
[{"affiliation": "The State Key Lab. LIESMARS, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "\u00b4Ecole Polytechnique F\u00b4ed\u00b4erale de Lausanne (EPFL)", "city": "Lausanne", "country": "Switzerland"}, {"affiliation": "Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "China University of Geosciences", "city": "Wuhan", "country": "China"}]

affiliations:
["The State Key Lab. LIESMARS, Wuhan University", "\u00b4Ecole Polytechnique F\u00b4ed\u00b4erale de Lausanne (EPFL)", "Chinese Academy of Sciences", "China University of Geosciences"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9186-9198
affiliations: ["Department of Computer Science, Tsinghua University"]
cities: ["Beijing"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_COUNTS_Benchmarking_Object_Detectors_and_Multimodal_Large_Language_Models_under_CVPR_2025_paper.html

title:
COUNTS: Benchmarking Object Detectors and Multimodal Large Language Models under Distribution Shifts

authors:
Jiansheng Li, Xingxuan Zhang, Hao Zou, Yige Guo, Renzhe Xu, Yilong Liu, Chuzhao Zhu, Yue He, Peng Cui

abstract:
Current object detectors often suffer significant performance degradation in real-world applications when encountering distributional shifts, posing serious risks in high-stakes domains such as autonomous driving and medical diagnosis. Consequently, the out-of-distribution (OOD) generalization capability of object detectors has garnered increasing attention from researchers. Despite this growing interest, there remains a lack of a large-scale, comprehensive dataset and evaluation benchmark with fine-grained annotations tailored to assess the OOD generalization on more intricate tasks like object detection and grounding. To address this gap, we introduce COUNTS, a large-scale OOD dataset with object-level annotations. COUNTS encompasses 14 natural distributional shifts, over 222K samples, and more than 1,196K labeled bounding boxes. Leveraging COUNTS, we introduce two novel benchmarks: O(OD) and OODG. OODOD is designed to comprehensively evaluate the OOD generalization capabilities of object detectors by utilizing controlled distribution shifts between training and testing data. OODG, on the other hand, aims to assess the OOD generalization of grounding abilities in multimodal large language models (MLLMs). Our findings reveal that, while large models and extensive pre-training data substantially enhance performance in in-distribution (IID) scenarios, significant limitations and opportunities for improvement persist in OOD contexts for both object detectors and MLLMs. In visual grounding tasks, even the advanced GPT-4o and Gemini-1.5 only achieve 56.7% and 28.0% accuracy, respectively. We hope COUNTS facilitates advancements in the development and assessment of robust object detectors and MLLMs capable of maintaining high performance under distributional shifts.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_COUNTS_Benchmarking_Object_Detectors_and_Multimodal_Large_Language_Models_under_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Jiansheng and Zhang, Xingxuan and Zou, Hao and Guo, Yige and Xu, Renzhe and Liu, Yilong and Zhu, Chuzhao and He, Yue and Cui, Peng},
    title     = {COUNTS: Benchmarking Object Detectors and Multimodal Large Language Models under Distribution Shifts},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9186-9198}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science, Tsinghua University", "city": "Beijing", "country": "China"}]


title: Retrieving Semantics from the Deep: an RAG Solution for Gesture Synthesis
authors: M. Hamza Mughal, Rishabh Dabral, Merel C.J. Scholman, Vera Demberg, Christian Theobalt
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16578-16588
affiliations: ["Max Planck Institute for Informatics, SIC", "Saarland University", "Utrecht University"]
cities: ["Saarbr\u00fccken", "Saarbr\u00fccken", "Utrecht"]
countries: ["Germany", "Germany", "Netherlands"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mughal_Retrieving_Semantics_from_the_Deep_an_RAG_Solution_for_Gesture_CVPR_2025_paper.html

abstract:
Non-verbal communication often comprises of semantically rich gestures that help convey the meaning of an utterance. Producing such semantic co-speech gestures has been a major challenge for the existing neural systems that can generate rhythmic beat gestures, but struggle to produce semantically meaningful gestures. Therefore, we present RAG-Gesture, a diffusion-based gesture generation approach that leverages Retrieval Augmented Generation (RAG) to produce natural-looking and semantically rich gestures. Our neuro-explicit gesture generation approach is designed to produce semantic gestures grounded in interpretable linguistic knowledge. We achieve this by using explicit domain knowledge to retrieve exemplar motions from a database of co-speech gestures. Once retrieved, we then inject these semantic exemplar gestures into our diffusion-based gesture generation pipeline using DDIM inversion and retrieval guidance at the inference time without any need of training. Further, we propose a control paradigm for guidance, that allows the users to modulate the amount of influence each retrieval insertion has over the generated sequence. Our comparative evaluations demonstrate the validity of our approach against recent gesture generation approaches. The reader is urged to explore the results on https://vcai.mpi-inf.mpg.de/projects/RAG-Gesture/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mughal_Retrieving_Semantics_from_the_Deep_an_RAG_Solution_for_Gesture_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mughal_Retrieving_Semantics_from_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Mughal_2025_CVPR,
    author    = {Mughal, M. Hamza and Dabral, Rishabh and Scholman, Merel C.J. and Demberg, Vera and Theobalt, Christian},
    title     = {Retrieving Semantics from the Deep: an RAG Solution for Gesture Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16578-16588}
}

raw_affiliation:
[{"affiliation": "Max Planck Institute for Informatics, SIC", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "Saarland University", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "Utrecht University", "city": "Utrecht", "country": "Netherlands"}]


title: HOT: Hadamard-based Optimized Training
authors: Seonggon Kim, Juncheol Shin, Seung-taek Woo, Eunhyeok Park
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4787-4796
cities: ["Pohang", "Pohang"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_HOT_Hadamard-based_Optimized_Training_CVPR_2025_paper.html

abstract:
It has become increasingly important to optimize backpropagation to reduce memory usage and computational overhead. Achieving this goal is highly challenging, as multiple objectives must be considered jointly while maintaining training quality. In this paper, we focus on matrix multiplication, which accounts for the largest portion of training costs, and analyze its backpropagation in detail to identify lightweight techniques that offer the best benefits. Based on this analysis, we introduce a novel method, Hadamard-based Optimized Training (HOT). In this approach, we apply Hadamard-based optimizations, such as Hadamard quantization and Hadamard low-rank approximation, selectively and with awareness of the suitability of each optimization for different backward paths. Additionally, we introduce two enhancements: activation buffer compression and layer-wise quantizer selection. Our extensive analysis shows that HOT achieves up to 75% memory savings and a 2.6 times acceleration on real GPUs, with negligible accuracy loss compared to FP32 precision.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_HOT_Hadamard-based_Optimized_Training_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_HOT_Hadamard-based_Optimized_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Seonggon and Shin, Juncheol and Woo, Seung-taek and Park, Eunhyeok},
    title     = {HOT: Hadamard-based Optimized Training},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4787-4796}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science and Engineering, POSTECH", "city": "Pohang", "country": "South Korea"}, {"affiliation": "Graduate School of Artificial Intelligence, POSTECH", "city": "Pohang", "country": "South Korea"}]

affiliations:
["Department of Computer Science and Engineering, POSTECH", "Graduate School of Artificial Intelligence, POSTECH"]


authors: Rohit Kundu, Hao Xiong, Vishal Mohanty, Athula Balachandran, Amit K. Roy-Chowdhury
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28050-28060
affiliations: ["Google LLC", "University of California, Riverside"]
cities: ["Mountain View", "Riverside"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kundu_Towards_a_Universal_Synthetic_Video_Detector_From_Face_or_Background_CVPR_2025_paper.html

title:
Towards a Universal Synthetic Video Detector: From Face or Background Manipulations to Fully AI-Generated Content

abstract:
Existing DeepFake detection techniques primarily focus on facial manipulations, such as face-swapping or lip-syncing. However, advancements in text-to-video (T2V) and image-to-video (I2V) generative models now allow fully AI-generated synthetic content and seamless background alterations, challenging face-centric detection methods and demanding more versatile approaches.To address this, we introduce the Universal Network for Identifying Tampered and Engineered videos (UNITE) model, which, unlike traditional detectors, captures full-frame manipulations. UNITE extends detection capabilities to scenarios without faces, non-human subjects, and complex background modifications. It leverages a transformer-based architecture that processes domain-agnostic features extracted from videos via the SigLIP-So400M foundation model. Given limited datasets encompassing both facial/background alterations and T2V/I2V content, we integrate task-irrelevant data alongside standard DeepFake datasets in training. We further mitigate the model's tendency to over-focus on faces by incorporating an attention-diversity (AD) loss, which promotes diverse spatial attention across video frames. Combining AD loss with cross-entropy improves detection performance across varied contexts. Comparative evaluations demonstrate that UNITE outperforms state-of-the-art detectors on datasets (in cross-data settings) featuring face/background manipulations and fully synthetic T2V/I2V videos, showcasing its adaptability and generalizable detection capabilities.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kundu_Towards_a_Universal_Synthetic_Video_Detector_From_Face_or_Background_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kundu_Towards_a_Universal_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Kundu_2025_CVPR,
    author    = {Kundu, Rohit and Xiong, Hao and Mohanty, Vishal and Balachandran, Athula and Roy-Chowdhury, Amit K.},
    title     = {Towards a Universal Synthetic Video Detector: From Face or Background Manipulations to Fully AI-Generated Content},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28050-28060}
}

raw_affiliation:
[{"affiliation": "Google LLC", "city": "Mountain View", "country": "USA"}, {"affiliation": "University of California, Riverside", "city": "Riverside", "country": "USA"}]


title: TokenFlow: Unified Image Tokenizer for Multimodal Understanding and Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2545-2555
raw_affiliation: [{"affiliation": "ByteDance", "city": "Beijing", "country": "China"}]
affiliations: ["ByteDance"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qu_TokenFlow_Unified_Image_Tokenizer_for_Multimodal_Understanding_and_Generation_CVPR_2025_paper.html

authors:
Liao Qu, Huichao Zhang, Yiheng Liu, Xu Wang, Yi Jiang, Yiming Gao, Hu Ye, Daniel K. Du, Zehuan Yuan, Xinglong Wu

abstract:
We present TokenFlow, a novel unified image tokenizer that bridges the long-standing gap between multimodal understanding and generation. Prior research attempt to employ a single reconstruction-targeted Vector Quantization (VQ) encoder for unifying these two tasks. We  observe that understanding and generation require fundamentally different granularities of visual information. This leads to a critical trade-off, particularly compromising performance in multimodal understanding tasks. TokenFlow addresses this challenge through an innovative dual-codebook architecture that decouples semantic and pixel-level feature learning while maintaining their alignment via a shared mapping mechanism. This design enables direct access to both high-level semantic representations crucial for understanding tasks and fine-grained visual features essential for generation through shared indices. Our extensive experiments demonstrate TokenFlow's superiority across multiple dimensions. Leveraging TokenFlow, we demonstrate for the first time that discrete visual input can surpass LLaVA-1.5 13B in understanding performance, achieving a 7.2% average improvement. For image reconstruction, we achieve a strong FID score of 0.63 at 384x384 resolution. Moreover, TokenFlow establishes state-of-the-art performance in autoregressive image generation with a GenEval score of 0.55 at 256x256 resolution, achieving comparable results to SDXL. Our code and models are released at https://github.com/ByteFlow-AI/TokenFlow.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qu_TokenFlow_Unified_Image_Tokenizer_for_Multimodal_Understanding_and_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qu_TokenFlow_Unified_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qu_2025_CVPR,
    author    = {Qu, Liao and Zhang, Huichao and Liu, Yiheng and Wang, Xu and Jiang, Yi and Gao, Yiming and Ye, Hu and Du, Daniel K. and Yuan, Zehuan and Wu, Xinglong},
    title     = {TokenFlow: Unified Image Tokenizer for Multimodal Understanding and Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2545-2555}
}


