pwc-archive/papers-with-abstracts huggingface dataset:
RePerformer: Immersive Human-centric Volumetric Videos from Playback to Photoreal Reperformance
Authors: Yuheng Jiang, Zhehao Shen, Chengcheng Guo, Yu Hong, Zhuo Su, Yingliang Zhang, Marc Habermann, Lan Xu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Attribute, Position

    Human-centric volumetric videos offer immersive free-viewpoint experiences, yet existing methods focus either on replaying general dynamic scenes or animating human avatars, limiting their ability to re-perform general dynamic scenes. In this paper, we present RePerformer, a novel Gaussian-based representation that unifies playback and re-performance for high-fidelity human-centric volumetric videos. Specifically, we hierarchically disentangle the dynamic scenes into motion Gaussians and appearance Gaussians which are associated in the canonical space. We further employ a Morton-based parameterization to efficiently encode the appearance Gaussians into 2D position and attribute maps. For enhanced generalization, we adopt 2D CNNs to map position maps to attribute maps, which can be assembled into appearance Gaussians for high-fidelity rendering of the dynamic scenes. For re-performance, we develop a semantic-aware alignment module and apply deformation transfer on motion Gaussians, enabling photo-real rendering under novel motions. Extensive experiments validate the robustness and effectiveness of RePerformer, setting a new benchmark for playback-then-reperformance paradigm in human-centric volumetric videos.
MNE-SLAM: Multi-Agent Neural SLAM for Mobile Robots
Authors: Tianchen Deng, Guole Shen, Chen Xun, Shenghai Yuan, Tongxin Jin, Hongming Shen, Yanbo Wang, Jingchuan Wang, Hesheng Wang, Danwei Wang, Weidong Chen
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: NeRF

    Neural implicit scene representations have recently shown promising results in dense visual SLAM. However, existing implicit SLAM algorithms are constrained to single-agent scenarios, and fall difficulty in large indoor scenes and long sequences. Existing multi-agent SLAM frameworks cannot meet the constraints of communication bandwidth. To this end, we propose the first distributed multi-agent collaborative SLAM framework with distributed mapping and camera tracking, joint scene representation, intra-to-inter loop closure, and multi-submap fusion. Specifically, our proposed distributed neural mapping and tracking framework only needs peer-to-peer communication, which can greatly improve multi-agent cooperation and communication efficiency. A novel intra-to-inter loop closure method is designed to achieve local (single-agent) and global (multi-agent) consistency. Furthermore, to the best of our knowledge, there is no real-world dataset for NeRF-based/GS-based SLAM that provides both continuous-time trajectories groundtruth and high-accuracy 3D meshes groundtruth. To this end, we propose the first real-world indoor neural slam (INS) dataset covering both single-agent and multi-agent scenarios, ranging from small room to large-scale scenes, with high-accuracy ground truth for both 3D mesh and continuous-time camera trajectory. This dataset can advance the development of the community. Experiments on various datasets demonstrate the superiority of the proposed method in both mapping, tracking, and communication. The dataset and code will be open-source on https://github.com/dtc111111/MNESLAM.
ADU: Adaptive Detection of Unknown Categories in Black-Box Domain Adaptation
Authors: Yushan Lai, Guowen Li, Haoyuan Liang, Juepeng Zheng, Zhiyu Ye
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Domain Adaptation, Knowledge Distillation, Unsupervised Domain Adaptation

    Black-box Domain Adaptation (BDA) utilizes a black-box predictor of the source domain to label target domain data, addressing privacy concerns in Unsupervised Domain Adaptation (UDA).  However, BDA assumes identical label sets across domains, which is unrealistic. To overcome this limitation, we propose a study on BDA with unknown classes in the target domain. It uses a black-box predictor to label target data and identify "unknown" categories, without requiring access to source domain data or predictor parameters, thus addressing both data privacy and category shift issues in traditional UDA. Existing methods face two main challenges: (i) Noisy pseudo-labels in knowledge distillation (KD) accumulate prediction errors, and (ii) relying on a preset threshold fails to adapt to varying category shifts. To address these, we propose ADU, a framework that allows the target domain to autonomously learn pseudo-labels guided by quality and use an adaptive threshold to identify "unknown" categories. Specifically, ADU consists of Selective Amplification Knowledge Distillation (SAKD) and Entopy-Driven Label Differentiation (EDLD). SAKD improves KD by focusing on high-quality pseudo-labels, mitigating the impact of noisy labels. EDLD categorizes pseudo-labels by quality and applies tailored training strategies to distinguish "unknown" categories, improving detection accuracy and adaptability. Extensive experiments show that ADU achieves state-of-the-art results, outperforming the best existing method by 3.1% on VisDA in the OPBDA scenario.
Understanding Multi-Task Activities from Single-Task Videos
Authors: YuHan Shen, Ehsan Elhamifar
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Action Segmentation, Segmentation, Temporal Action Segmentation

    (MT-TAS), a novel paradigm that addresses the challenges of interleaved actions when performing multiple tasks simultaneously. Traditional action segmentation models, trained on single-task videos, struggle to handle task switches and complex scenes inherent in multi-task scenarios. To overcome these challenges, our MT-TAS approach synthesizes multi-task video data from single-task sources using our Multi-task Sequence Blending and Segment Boundary Learning modules. Additionally, we propose to dynamically isolate foreground and background elements within video frames, addressing the intricacies of object layouts in multi-task scenarios and enabling a new two-stage temporal action segmentation framework with Foreground-Aware Action Refinement. Also, we introduce the Multi-task Egocentric Kitchen Activities (MEKA) dataset, containing 12 hours of egocentric multi-task videos, to rigorously benchmark MT-TAS models. Extensive experiments demonstrate that our framework effectively bridges the gap between single-task training and multi-task testing, advancing temporal action segmentation with state-of-the-art performance in complex environments.
Spherical Manifold Guided Diffusion Model for Panoramic Image Generation
Authors: Xiancheng Sun, Mai Xu, Shengxi Li, Senmao Ma, Xin Deng, Lai Jiang, Gang Shen
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: ERP, Image Generation

    Panoramic image essentially acts as a pivotal role in emerging virtual reality and augmented reality scenarios; however, the generation of panoramic images are essentially challenging due to the intrinsic spherical geometry and spherical distortions caused by equirectangular projection (ERP). To address this, we start from the very basics of S^2 manifold inherent to panoramic images, and propose a novel spherical manifold convolution (SMConv) on S^2 manifold. Based on the SMConv operation, we propose a spherical manifold guided diffusion (SMGD) model for text-conditioned panoramic image generation, which can well accommodate the spherical geometry during generation. We further develop a novel evaluation method by calculating grouped Frechet inception distance (FID) on cube-map projections, which can well reflect the quality of generated panoramic images, compared to existing methods that randomly crop ERP-distorted content. Experiment results demonstrate that our SMGD model achieves the state-of-the-art generation quality and accuracy, whilst retaining the shortest sampling time in the text-conditioned panoramic image generation task. Codes are publicly available at https://github.com/chronos123/SMGD.
EntropyMark: Towards More Harmless Backdoor Watermark via Entropy-based Constraint for Open-source Dataset Copyright Protection
Authors: Ming Sun, Rui Wang, Zixuan Zhu, Lihua Jing, Yuanfang Guo
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Prediction

    High-quality open-source datasets are essential for advancing deep neural networks. However, the unauthorized commercial use of these datasets has raised significant concerns about copyright protection. One promising approach is backdoor watermark-based dataset ownership verification (BW-DOV), in which dataset protectors implant specific backdoors into illicit models through dataset watermarking, enabling the tracing of these models through abnormal prediction behaviors. Unfortunately, the targeted nature of these BW-DOV methods can be maliciously exploited, potentially leading to harmful side effects. While existing harmless methods attempt to mitigate these risks, watermarked datasets can still negatively affect prediction results, partially compromising dataset functionality. In this paper, we propose a more harmless backdoor watermark, called EntropyMark, which improves prediction confidence without altering the final prediction results. For this purpose, an entropy-based constraint is introduced to regulate the probability distribution. Specifically, we design an iterative clean-label dataset watermarking framework. Our framework employs gradient matching and adaptive data selection to optimize backdoor injection. In parallel, we introduce a hypothesis test method grounded in entropy inconsistency to verify dataset ownership. Extensive experiments on benchmark datasets demonstrate the effectiveness, transferability, and defense resistance of our approach.
Unveiling Differences in Generative Models: A Scalable Differential Clustering Approach
Authors: Jingwei Zhang, Mohammad Jalali, Cheuk Ting Li, Farzan Farnia
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Clustering

    A fine-grained comparison of generative models requires the identification of sample types generated differently by each of the involved models. While quantitative scores have been proposed in the literature to rank different generative models, score-based evaluation and ranking do not reveal the nuanced differences between the generative models in producing different sample types. In this work, we propose solving a differential clustering problem to detect sample types generated differently by two generative models. To solve the differential clustering problem, we develop a spectral method called Fourier-based Identification of Novel Clusters (FINC) to identify modes produced by a generative model with a higher frequency in comparison to a reference distribution. FINC provides a scalable algorithm based on random Fourier features to estimate the eigenspace of kernel covariance matrices of two generative models and utilize the principal eigendirections to detect the sample types present more dominantly in each model. We demonstrate the application of the FINC method to large-scale computer vision datasets and generative modeling frameworks. Our numerical results suggest the scalability of the developed Fourier-based method in highlighting the sample types produced with different frequencies by generative models. The project code is available at https://github.com/buyeah1109/FINC
Learning Person-Specific Animatable Face Models from In-the-Wild Images via a Shared Base Model
Authors: Yuxiang Mao, Zhenfeng Fan, Zhijie Zhang, Zhiheng Zhang, Shihong Xia
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Face Reconstruction, Face Alignment, Face Reconstruction

    Training a generic 3D face reconstruction model in a self-supervised manner using large-scale, in-the-wild 2D face image datasets enhances robustness to varying lighting conditions and occlusions while allowing the model to capture animatable wrinkle details across diverse facial expressions. However, a generic model often fails to adequately represent the unique characteristics of specific individuals. In this paper, we propose a method to train a generic base model and then transfer it to yield person-specific models by integrating lightweight adapters within the large-parameter ViT-MAE base model. These person-specific models excel at capturing individual facial shapes and detailed features while preserving the robustness and prior knowledge of detail variations from the base model. During training, we introduce a silhouette vertex re-projection loss to address boundary "landmark marching" issues on the 3D face caused by pose variations. Additionally, we employ an innovative teacher-student loss to leverage the inherent strengths of UNet in feature boundary localization for training our detail MAE. Quantitative and qualitative experiments demonstrate that our approach achieves state-of-the-art performance in face alignment, detail accuracy, and richness. The source code is available at https://github.com/danielmao2000/person-specific-animatable-face.
Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention
Authors: Wenbin An, Feng Tian, Sicong Leng, Jiahao Nie, Haonan Lin, Qianying Wang, Ping Chen, Xiaoqin Zhang, Shijian Lu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Hallucination, Response Generation, Visual Grounding

    Despite great success across various multimodal tasks, Large Vision-Language Models (LVLMs) often encounter object hallucinations with generated textual responses being inconsistent with the actual objects in images. We examine different LVLMs and pinpoint that one root cause of object hallucinations lies with deficient attention on discriminative image features. Specifically, LVLMs often predominantly attend to prompt-irrelevant global features instead of prompt-relevant local features, undermining their visual grounding capacity and leading to object hallucinations. We propose Assembly of Global and Local Attention (AGLA) , a training-free and plug-and-play approach that mitigates hallucinations by assembling global features for response generation and local features for visual discrimination simultaneously.  Specifically, we introduce an image-prompt matching scheme that captures prompt-relevant local features from images, leading to an augmented view of the input image where prompt-relevant content is highlighted while irrelevant distractions are suppressed.  Hallucinations can thus be mitigated with a calibrated logit distribution that is from generative global features of the original image and discriminative local features of the augmented image. Extensive experiments show the superiority of AGLA in LVLM hallucination mitigation, demonstrating its wide applicability across both discriminative and generative tasks. Our code is available at https://github.com/Lackel/AGLA.
Chebyshev Attention Depth Permutation Texture Network with Latent Texture Attribute Loss
Authors: Ravishankar Evani, Deepu Rajan, Shangbo Mao
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Attribute, Material Recognition

    Despite recent advances in deep texture recognition, existing methods still lack representational diversity and struggle to capture and preserve discriminative cues across stages of representation hierarchies. Moreover, many rely on loss formulations that prioritize recognition accuracy while overlooking spatial coherence and statistical consistency in the feature space. To address these issues, we propose three key innovations: Stochastic Local Texture Masking (SLTM), a regularization strategy that randomly occludes small texture patches to promote the learning of broader spatial and contextual dependencies; the Chebyshev Attention Depth Permutation Texture Network (CAPTN), a novel architecture that learns expressive and persistent Latent Texture Attribute (LTA) representations. CAPTN integrates a Texture Frequency Attention (TFA) module that generates LTAs and enables frequency-aware interpretability, a Dual Depth Permutation (D2P) module to expose complementary channel adjacency patterns, and Learnable Chebyshev Polynomials (LCPs) to model high-order orderless LTA transformations via recursive Chebyshev basis expansion; and a Latent Texture Attribute Loss that jointly optimizes classification accuracy, statistical alignment, and spatial fidelity. CAPTN supports end-to-end training without relying on fine-tuned CNN backbones and achieves state-of-the-art performance on several texture and material recognition benchmarks. (Code: https://github.com/RavishankarEvani/CAPTN)
S2Gaussian: Sparse-View Super-Resolution 3D Gaussian Splatting
Authors: Yecong Wan, Mingwen Shao, Yuanshuo Cheng, WangMeng Zuo
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Super-Resolution

    In this paper, we aim ambitiously for a realistic yet challenging problem, namely, how to reconstruct high-quality 3D scenes from sparse low-resolution views that simultaneously suffer from deficient perspectives and contents. Whereas existing methods only deal with either sparse views or low-resolution observations, they fail to handle such hybrid and complicated scenarios. To this end, we propose a novel Sparse-view Super-resolution 3D Gaussian Splatting framework, dubbed S2Gaussian, that can reconstruct structure-accurate and detail-faithful 3D scenes with only sparse and low-resolution views. The S2Gaussian operates in a two-stage fashion. In the first stage, we initially optimize a low-resolution Gaussian representation with depth regularization and densify it to initialize the high-resolution Gaussians through a tailored Gaussian Shuffle operation. In the second stage, we refine the high-resolution Gaussians with the super-resolved images generated from both original sparse views and pseudo-views rendered by the low-resolution Gaussians. In which a customized blur-free inconsistency modeling scheme and a 3D robust optimization strategy are elaborately designed to mitigate multi-view inconsistency and eliminate erroneous updates caused by imperfect supervision. Extensive experiments demonstrate superior results and in particular establishing new state-of-the-art performances with more consistent geometry and finer details. Project Page https://jeasco.github.io/S2Gaussian/.
AdaCM^2: On Understanding Extremely Long-Term Video with Adaptive Cross-Modality Memory Reduction
Authors: Yuanbin Man, Ying Huang, Chengming Zhang, Bingzhe Li, Wei Niu, Miao Yin
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: GPU, Question Answering, Video Captioning, Video Classification, Video Question Answering, Video Understanding

    The advancements in large language models (LLMs) have propelled the improvement of video understanding tasks by incorporating LLMs with visual models. However, most existing LLM-based models (e.g., VideoLLaMA, VideoChat) are constrained to processing short-duration videos. Recent attempts to understand long-term videos by extracting and compressing visual features into a fixed memory size. Nevertheless, those methods leverage only visual modality to merge video tokens and overlook the correlation between visual and textual queries, leading to difficulties in effectively handling complex question-answering tasks. To address the challenges of long videos and complex prompts, we propose AdaCM^2, which, for the first time, introduces an adaptive cross-modality memory reduction approach to video-text alignment in an auto-regressive manner on video streams. Our extensive experiments on various video understanding tasks, such as video captioning, video question answering, and video classification, demonstrate that AdaCM^2 achieves state-of-the-art performance across multiple datasets while significantly reducing memory usage. Notably, it achieves a 4.5% improvement across multiple tasks in the LVU dataset with a GPU memory consumption reduction of up to 65%.
KeyFace: Expressive Audio-Driven Facial Animation for Long Sequences via KeyFrame Interpolation
Authors: Antoni Bigata, Michał Stypułkowski, Rodrigo Mira, Stella Bounareli, Konstantinos Vougioukas, Zoe Landgraf, Nikita Drobyshev, Maciej Zieba, Stavros Petridis, Maja Pantic
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    Current audio-driven facial animation methods achieve impressive results for short videos but suffer from error accumulation and identity drift when extended to longer durations. Existing methods attempt to mitigate this through external spatial control, increasing long-term consistency but compromising the naturalness of motion. We propose KeyFace, a novel two-stage diffusion-based framework, to address these issues. In the first stage, keyframes are generated at a low frame rate, conditioned on audio input and an identity frame, to capture essential facial expressions and movements over extended periods of time. In the second stage, an interpolation model fills in the gaps between keyframes, ensuring smooth transitions and temporal coherence. To further enhance realism, we incorporate continuous emotion representations and handle a wide range of non-speech vocalizations (NSVs), such as laughter and sighs. We also introduce two new evaluation metrics for assessing lip synchronization and NSV generation. Experimental results show that KeyFace outperforms state-of-the-art methods in generating natural, coherent facial animations over extended durations, successfully encompassing NSVs and continuous emotions.
Leveraging Global Stereo Consistency for Category-Level Shape and 6D Pose Estimation from Stereo Images
Authors: Junning Qiu, Minglei Lu, Fei Wang, Yu Guo, Yonggen Ling
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 6D Pose Estimation, Pose Estimation

    Stereo-based category-level shape and 6D pose estimation methods have the potential to generalize to a wider range of materials than RGBD methods, which often suffer from depth measurement errors. However, without explicit depth from two views,  parameters to be estimated can become inherently entangled, negatively impacting performance. To address this, we propose a method that leverages global stereo consistency to constrain optimization directions and mitigate parameter entanglement. We first estimate an intra-category occupancy field to represent a unified shape across views, ensuring consistency and preventing shape ambiguity. Through a divide-and-conquer approach within global shape fitting, we fit this shape to stereo images to obtain the pose, iteratively rendering normalized depth maps and exchanging information across views. This approach improves convergence toward the correct pose and scale. We validated our method on both depth-friendly and depth-challenging materials using our S-RGBD dataset and the TOD benchmark. Our method surpasses RGBD methods on challenging objects and performs comparably on depth-friendly ones. Ablation studies confirm the effectiveness of each component.