pwc-archive/papers-with-abstracts huggingface dataset:
POMP: Physics-consistent Motion Generative Model through Phase Manifolds
Authors: Bin Ji, Ye Pan, Zhimeng Liu, Shuai Tan, Xiaogang Jin, Xiaokang Yang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Motion Generation, Unity

    Numerous researches on real-time motion generation primarily focus on kinematic aspects, often resulting in physically implausible outcomes. In this paper, we present POMP ("\underline P hysics-c\underline O nsistent Human \underline M otion \underline P rior through Phase Manifolds"), a novel kinematics-based framework that synthesizes physically consistent motions by leveraging phase manifolds to align motion priors with physics constraints. POMP operates as a frame-by-frame autoregressive model with three core components: a diffusion-based kinematic module, a simulation-based dynamic module, and a phase encoding module. At each timestep, the kinematic module generates an initial target pose, which is subsequently refined by the dynamic module to simulate human-environment interactions. Although the physical simulation ensures adherence to physical laws, it may compromise the kinematic rationality of the posture. Consequently, directly using the simulated result for subsequent frame prediction may lead to cumulative errors. To address this, the phase encoding module performs semantic alignment in the phase manifold. Moreover, we present a pipeline in Unity for generating terrain maps and capturing full-body motion impulses from existing motion capture (MoCap) data. The collected terrain topology and motion impulse data facilitate the training of POMP, enabling it to robustly respond to underlying contactforces and applied dynamics. Extensive evaluations demonstrate the efficacy of POMP across various contexts, terrains, and physical interactions.
RCP-Bench: Benchmarking Robustness for Collaborative Perception Under Diverse Corruptions
Authors: Shihang Du, Sanqing Qu, Tianhang Wang, Xudong Zhang, Yunwei Zhu, Jian Mao, Fan Lu, Qiao Lin, Guang Chen
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Benchmarking

    Collaborative perception enhances single-vehicle perception by integrating sensory data from multiple connected vehicles. However, existing studies often assume ideal conditions, overlooking resilience to real-world challenges such as adverse weather and sensor malfunctions, which is critical for safe deployment. To address this gap, we introduce RCP-Bench, the first comprehensive benchmark designed to evaluate the robustness of collaborative detection models under a wide range of real-world corruptions. RCP-Bench includes three new datasets (i.e., OPV2V-C, V2XSet-C, and DAIR-V2X-C) that simulate six collaborative cases and 14 types of camera corruption resulting from external environmental factors, sensor failures, and temporal misalignments. Extensive experiments on 10 leading collaborative perception models reveal that, while these models perform well under ideal conditions, they are significantly affected by corruptions. To improve robustness, we propose two simple yet effective strategies, RCP-Drop and RCP-Mix, based on training regularization and feature augmentation. Additionally, we identify several critical factors influencing robustness, such as backbone architecture, camera number, feature fusion methods, and the number of connected vehicles. We hope that RCP-Bench, along with these strategies and insights, will stimulate future research toward developing more robust collaborative perception models. Our benchmark toolkit is available at https://github.com/LuckyDush/RCP-Bench.
Tartan IMU: A Light Foundation Model for Inertial Positioning in Robotics
Authors: Shibo Zhao, Sifan Zhou, Raphael Blanchard, Yuheng Qiu, Wenshan Wang, Sebastian Scherer
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: State Estimation, Test-time Adaptation

    Despite recent advances in deep learning, most existing learning IMU odometry methods are trained on specific datasets, lack generalization, and are prone to overfitting, which limits their real-world application. To address these challenges, we present Tartan IMU, a foundation model designed for generalizable, IMU-based state estimation across diverse robotic platforms. Our approach consists of three-stage: First, a pre-trained foundation model leverages over 100 hours of multi-platform data to establish general motion knowledge, achieving 36% improvement in ATE over specialized models. Second, to adapt to previously unseen tasks, we employ the Low-Rank Adaptation (LoRA), allowing positive transfer with only 1.1 M trainable parameters. Finally, to support robotics deployment, we introduce online test-time adaptation, which eliminates the boundary between training and testing, allowing the model to continuously "learn as it operates" at 200 FPS in real-time.
Evolving High-Quality Rendering and Reconstruction in a Unified Framework with Contribution-Adaptive Regularization
Authors: You Shen, Zhipeng Zhang, Xinyang Li, Yansong Qu, Yu Lin, Shengchuan Zhang, Liujuan Cao
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3DGS, Surface Reconstruction

    Representing 3D scenes from multiview images is a core challenge in computer vision and graphics, which requires both precise rendering and accurate reconstruction. Recently, 3D Gaussian Splatting (3DGS) has garnered significant attention for its high-quality rendering and fast inference speed. Yet, due to the unstructured and irregular nature of Gaussian point clouds, ensuring accurate geometry reconstruction remains difficult. Existing methods primarily focus on geometry regularization, with common approaches including primitive-based and dual-model frameworks. However, the former suffers from inherent conflicts between rendering and reconstruction, while the latter is computationally and storage-intensive. To address these challenges, we propose CarGS, a unified model leveraging Contribution adaptive regularization to achieve simultaneous, high-quality rendering and surface reconstruction. The essence of our framework is learning adaptive contribution for Gaussian primitives by squeezing the knowledge from geometry regularization into a compact MLP. Additionally, we introduce a geometry-guided densification strategy with clues from both normals and Signed Distance Fields (SDF) to improve the capability of capturing high-frequency details. Our design improves the mutual learning of the two tasks, meanwhile its unified structure doesn't require separate models as in dual-model based approaches, guaranteeing efficiency. Extensive experiments demonstrate CarGS's ability to achieve state-of-the-art (SOTA) results in both rendering fidelity and reconstruction accuracy while maintaining real-time speed and minimal storage size.
AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models
Authors: Run He, Kai Tong, Di Fang, Han Sun, Ziqian Zeng, Haoran Li, Tianyi Chen, Huiping Zhuang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Federated Learning

    In this paper, we introduce analytic federated learning (AFL), a new training paradigm that brings analytical (i.e., closed-form) solutions to the federated learning (FL) with pre-trained models. Our AFL draws inspiration from analytic learning---a gradient-free technique that trains neural networks with analytical solutions in one epoch. In the local client training stage, the AFL facilitates a one-epoch training,  eliminating the necessity for multi-epoch updates. In the aggregation stage, we derive an absolute aggregation (AA) law. This AA law allows a single-round aggregation, reducing heavy communication overhead and achieving fast convergence by removing the need for multiple aggregation rounds. More importantly, the AFL exhibits a property that invariance to data partitioning, meaning that regardless of how the full dataset is distributed among clients, the aggregated result remains identical. This could spawn various potentials, such as data heterogeneity invariance and client-number invariance. We conduct experiments across various FL settings including extremely non-IID ones, and scenarios with a large number of clients (e.g., \ge 1000). In all these settings, our AFL constantly performs competitively while existing FL techniques encounter various obstacles.
Tuning the Frequencies: Robust Training for Sinusoidal Neural Networks
Authors: Tiago Novello, Diana Aldana, Andre Araujo, Luiz Velho
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    Sinusoidal neural networks have been shown effective as implicit neural representations (INRs) of low-dimensional signals, due to their smoothness and high representation capacity. However, initializing and training them remain empirical tasks which lack on deeper understanding to guide the learning process. To fill this gap, our work introduces a theoretical framework that explains the capacity property of sinusoidal networks and offers robust control mechanisms for initialization and training. Our analysis is based on a novel amplitude-phase expansion of the sinusoidal multilayer perceptron, showing how its layer compositions produce a large number of new frequencies expressed as integer combinations of the input frequencies. This relationship can be directly used to initialize the input neurons, as a form of spectral sampling, and to bound the network's spectrum while training. Our method, referred to as TUNER (TUNing sinusoidal nEtwoRks), greatly improves the stability and convergence of sinusoidal INR training, leading to detailed reconstructions, while preventing overfitting.
S2D-LFE: Sparse-to-Dense Light Field Event Generation
Authors: Yutong Liu, Wenming Weng, Yueyi Zhang, Zhiwei Xiong
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Novel View Synthesis

    In this paper, we present S2D-LFE, an innovative approach for sparse-to-dense light field event generation. For the first time to our knowledge, S2D-LFE enables controllable novel view synthesis only from sparse-view light field event (LFE) data, and addresses three critical challenges for the LFE generation task: simplicity, controllability, and consistency. The simplicity aspect eliminates the dependency on frame-based modality, which often suffers from motion blur and low frame-rate limitations. The controllability aspect enables precise view synthesis under sparse LFE conditions with view-related constraints. The consistency aspect ensures both cross-view and temporal coherence in the generated results. To realize S2D-LFE, we develop a novel diffusion-based generation network with two key components. First, we design an LFE-customized variational auto-encoder that effectively compresses and reconstructs LFE by integrating cross-view information. Second, we design an LFE-aware injection adaptor to extract comprehensive geometric and texture priors. Furthermore, we construct a large-scale synthetic LFE dataset containing 162 one-minute sequences using simulator, and capture a real-world testset using our custom-built sparse LFE acquisition system, covering diverse indoor and outdoor scenes. Extensive experiments demonstrate that S2D-LFE successfully generates up to 9x9 dense LFE from sparse 2x2 inputs and outperforms existing methods on both synthetic and real-world data. The datasets and code are available at https://github.com/Yutong2022/S2D-LFE.
VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging
Authors: Yufan He, Pengfei Guo, Yucheng Tang, Andriy Myronenko, Vishwesh Nath, Ziyue Xu, Dong Yang, Can Zhao, Benjamin Simon, Mason Belue, Stephanie Harmon, Baris Turkbey, Daguang Xu, Wenqi Li
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Interactive Segmentation, Segmentation

    Foundation models for interactive segmentation in 2D natural images and videos have sparked significant interest in building 3D foundation models for medical imaging. However, the domain gaps and clinical use cases for 3D medical imaging require a dedicated model that diverges from existing 2D solutions. Specifically, such foundation models should support a full workflow that can actually reduce human effort. Treating 3D medical images as sequences of 2D slices and reusing interactive 2D foundation models seems straightforward, but 2D annotation is too time-consuming for 3D tasks. Moreover, for large cohort analysis, it's the highly accurate automatic segmentation models that reduce the most human effort. However, these models lack support for interactive corrections and lack zero-shot ability for novel structures, which is a key feature of "foundation". While reusing pre-trained 2D backbones in 3D enhances zero-shot potential, their performance on complex 3D structures still lags behind leading 3D models. To address these issues, we present VISTA3D, Versatile Imaging SegmenTation and Annotation model, that targets to solve all thesechallenges and requirements with one unified foundation model. VISTA3D is built on top of the well-established 3D segmentation pipeline, and it is the first model to achieve state-of-the-art performance in both 3D automatic (supporting 127 classes) and 3D interactive segmentation, even when compared with top 3D expert models on large and diverse benchmarks. Additionally, VISTA3D's 3D interactive design allows efficient human correction, and a novel 3D supervoxel method that distills 2D pretrained backbones grants VISTA3D top 3D zero-shot performance. We believe the model, recipe, and insights represent a promising step toward a clinically useful 3D foundation model. Code and weights are publicly available at https://github.com/Project-MONAI/VISTA
SGSST: Scaling Gaussian Splatting Style Transfer
Authors: Bruno Galerne, Jianling Wang, Lara Raad, Jean-Michel Morel
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3DGS, Neural Rendering, Style Transfer

    Applying style transfer to a full 3D environment is a challenging task that has seen many developments since the advent of neural rendering. 3D Gaussian splatting (3DGS) has recently pushed further many limits of neural rendering in terms of training speed and reconstruction quality. This work introduces SGSST: Scaling Gaussian Splatting Style Transfer, an optimization-based method to apply style transfer to pretrained 3DGS scenes. We demonstrate that a new multiscale loss based on global neural statistics, that we name SOS for Simultaneously Optimized Scales, enables style transfer to ultra-high resolution 3D scenes. Not only SGSST pioneers 3D scene style transfer at such high image resolutions, it also produces superior visual quality as assessed by thorough qualitative, quantitative and perceptual comparisons.
Parameterized Blur Kernel Prior Learning for Local Motion Deblurring
Authors: Zhenxuan Fang, Fangfang Wu, Tao Huang, Le Dong, Weisheng Dong, Xin Li, Guangming Shi
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Deblurring, Mamba

    Unlike global motion blur, Local Motion Deblurring (LMD) presents a more complex challenge, as it requires precise restoration of blurry regions while preserving the sharpness of the background. Existing LMD methods rely on manually annotated blur masks and often overlook the blur kernel's characteristics, which are crucial for accurate restoration. To address these limitations, we propose a novel parameterized motion kernel modeling approach that defines the motion blur kernel with three key parameters: length, angle, and curvature. We then use networks to estimate these kernel parameters, significantly improving the accuracy of blur kernel estimation. To effectively learn the motion blur representation, we incorporate a shared memory bank that stores blur prior information. Additionally, we introduce a dual-branch deblurring network: one branch leverages Mamba to capture long-range dependencies, while the other uses a mask-guided CNN focused on refining the local blurry regions. By fully utilizing the estimated bur prior information, our approach greatly enhances deblurring outcomes. Experimental results show that our method achieves state-of-the-art performance both quantitatively and visually, with a substantial reduction in computational complexity.
Rethinking Reconstruction and Denoising in the Dark: New Perspective, General Architecture and Beyond
Authors: Tengyu Ma, Long Ma, Ziye Li, Yuetong Wang, JinYuan Liu, Chengpei Xu, Risheng Liu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Denoising

    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.
AuraFusion360: Augmented Unseen Region Alignment for Reference-based 360deg Unbounded Scene Inpainting
Authors: Chung-Ho Wu, Yang-Jung Chen, Ying-Huan Chen, Jie-Ying Lee, Bo-Hsu Ke, Chun-Wei Tuan Mu, Yi-Chuan Huang, Chin-Yang Lin, Min-Hung Chen, Yen-Yu Lin, Yu-Lun Liu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    Three-dimensional scene inpainting is crucial for applications from virtual reality to architectural visualization, yet existing methods struggle with view consistency and geometric accuracy in 360deg unbounded scenes. We present AuraFusion360, a novel reference-based method that enables high-quality object removal and hole filling in 3D scenes represented by Gaussian Splatting. Our approach introduces (1) depth-aware unseen mask generation for accurate occlusion identification, (2) Adaptive Guided Depth Diffusion, a zero-shot method for accurate initial point placement without requiring additional training, and (3) SDEdit-based detail enhancement for multi-view coherence. We also introduce 360-USID, the first comprehensive dataset for 360deg unbounded scene inpainting with ground truth. Extensive experiments demonstrate that AuraFusion360 significantly outperforms existing methods, achieving superior perceptual quality while maintaining geometric accuracy across dramatic viewpoint changes.
STDD: Spatio-Temporal Dual Diffusion for Video Generation
Authors: Shuaizhen Yao, Xiaoya Zhang, Xin Liu, Mengyi Liu, Zhen Cui
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Text-to-Video Generation, Video Generation

    Diffusion probabilistic model is becoming the cornerstone of data generation, especially generating high-quality images. As an extension, video diffusion generation is in urgent need of a principled temporal-sequence diffusion way, while the spatial-domain diffusion dominates most video diffusion methods. In this work, we propose an explicit Spatio-Temporal Dual Diffusion (STDD) method by principledly extending the standard diffusion model to a spatio-temporal diffusion model for joint spatial and temporal noise propagation/reduction. Mathematically, an analysable dual diffusion process is derived to accumulate noises/information in temporal sequence as well as spatial domain. Correspondingly, we theoretically derive a spatio-temporal probabilistic reverse diffusion process and propose an accelerated sampling way to reduce the inference cost.  In principle, the spatio-temporal dual diffusion enables the information of previous frames to be transferred to the current frame, which thus could be beneficial for video consistency. Extensive experiments demonstrate that our proposed STDD is more competitive over the state-of-the-art methods in the task of video generation/prediction as well as text-to-video generation.
Improving Semi-Supervised Semantic Segmentation with Sliced-Wasserstein Feature Alignment and Uniformity
Authors: Chen-Yi Lu, Kasra Derakhshandeh, Somali Chaterji
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Segmentation, Semantic Segmentation, Semi-Supervised Semantic Segmentation

    Semi-supervised semantic segmentation with consistencyregularization capitalizes on unlabeled images to enhancethe accuracy of pixel-level segmentation. Current consistencylearning methods primarily rely on the consistency loss be-tween pseudo-labels and unlabeled images, neglecting the in-formation within the feature representations of the backboneencoder. Preserving maximum information in feature embed-dings requires achieving the alignment and uniformity objec-tives, as widely studied. To address this, we present SWSEG,a semi-supervised semantic segmentation algorithm that opti-mizes alignment and uniformity using the Sliced-WassersteinDistance (SWD), and rigorously and empirically proves thisconnection. We further resolve the computational issues as-sociated with conventional Monte Carlo-based SWD by im-plementing a Gaussian-approximated variant, which not onlymaintains the alignment and uniformity objectives but alsoimproves training efficiency. We evaluate SWSEG on thePASCAL VOC 2012, Cityscapes, and ADE20K datasets, out-shining supervised baselines in mIoU by up to 11.8%, 8.9%,and 8.2%, respectively, given an equivalent number of labeledsamples. Further, SWSEG surpasses state-of-the-art methodsin multiple settings across these three datasets. Our extensiveablation studies confirm the optimization of the uniformityand alignment objectives of the feature representations.
HyperSeg: Hybrid Segmentation Assistant with Fine-grained Visual Perceiver
Authors: Cong Wei, Yujie Zhong, Haoxian Tan, Yong liu, Jie Hu, Dengjie Li, Zheng Zhao, Yujiu Yang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Reasoning Segmentation, Segmentation, Universal Segmentation, Video Segmentation, Video Semantic Segmentation, World Knowledge

    This paper aims to address universal segmentation for image and video perception with the strong reasoning ability empowered by Visual Large Language Models (VLLMs). Despite significant progress in current unified segmentation methods, limitations in adaptation to both image and video scenarios, as well as the complex reasoning segmentation, make it difficult for them to handle various challenging instructions and achieve an accurate understanding of fine-grained vision-language correlations. We propose HyperSeg, the first VLLM-based universal segmentation model for pixel-level image and video perception, encompassing generic segmentation tasks and more complex reasoning perception tasks requiring powerful reasoning abilities and world knowledge. Besides, to fully leverage the recognition capabilities of VLLMs and the fine-grained visual information, HyperSeg incorporates hybrid entity recognition and fine-grained visual perceiver modules for various segmentation tasks. Combined with the temporal adapter, HyperSeg achieves a comprehensive understanding of temporal information. Experimental results validate the effectiveness of our insights in resolving universal image and video segmentation tasks, including the more complex reasoning perception tasks. Our code is available at https://github.com/congvvc/HyperSeg.