pwc-archive/papers-with-abstracts huggingface dataset:
Diffusion-based Realistic Listening Head Generation via Hybrid Motion Modeling
Authors: Yinuo Wang, Yanbo Fan, Xuan Wang, Guo Yu, Fei Wang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Motion Generation, Video Generation

    Listening head generation aims to synthesize non-verbal responsive listening head videos that naturally react to a certain speaker, for which, both realistic head movements, expressive facial expressions, and high visual qualities are expected. Previous approaches typically follow a two-stage pipeline that first generates intermediate 3D motion signals such as 3DMM coefficients, and then synthesizes the videos by deterministic rendering, suffering from limited motion expressiveness and low visual quality (eg, 256 x256). In this work, we propose a novel listening head generation method that harnesses the generative capabilities of the diffusion model for both motion generation and high-quality rendering. Crucially, we propose an effective hybrid motion modeling module that addresses training difficulties caused by the scarcity of listening head data while preserving the intricate details that may be lost in explicit motion representations. We further develop a tailored control guidance for head pose and facial expression, by integrating their intrinsic motion characteristics. Our method enables high-fidelity video generation with 512x512 resolution and delivers vivid listener motion feedback. We conduct comprehensive experiments and obtain superior performance in terms of both visual quality and motion expressiveness compared with existing methods.
Keyframe-Guided Creative Video Inpainting
Authors: Yuwei Guo, Ceyuan Yang, Anyi Rao, Chenlin Meng, Omer Bar-Tal, Shuangrui Ding, Maneesh Agrawala, Dahua Lin, Bo Dai
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Diversity, text-guided-generation, Video Inpainting

    Video inpainting, which aims to fill missing regions with visually coherent content, has emerged as a crucial technique for creative applications such as editing. While existing approaches achieve visual consistency or text-guided generation, they often struggle to balance coherence and creative diversity. In this work, we introduce VideoRepainter, a two-stage framework that allows users to inpaint a keyframe using established image-level techniques, then propagate the changes to other frames. Our approach can leverage state-of-the-art image models for keyframe manipulation, thereby easing the burden of the video-inpainting process. To this end, we integrate an image-to-video model with a symmetric condition mechanism to address ambiguity caused by direct mask downsampling. We further explore efficient strategies for mask synthesis and parameter tuning to reduce costs in data processing and model training. Evaluations demonstrate our method achieves superior results in both visual fidelity and content diversity compared to existing approaches, providing a practical solution for creative video manipulation.
Neural Motion Simulator Pushing the Limit of World Models in Reinforcement Learning
Authors: Chenjie Hao, Weyl Lu, Yifan Xu, Yubei Chen
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

    An embodied system must not only model the patterns of the external world but also understand its own motion dynamics. A motion dynamic model is essential for efficient skill acquisition and effective planning. In this work, we introduce the neural motion simulator (MoSim), a world model that predicts the future physical state of an embodied system based on current observations and actions. MoSim achieves state-of-the-art performance in physical state prediction and provides competitive performance across a range of downstream tasks. This works shows that when a world model is accurate enough and performs precise long-horizon predictions, it can facilitate efficient skill acquisition in imagined worlds and even enable zero-shot reinforcement learning. Furthermore, MoSim can transform any model-free reinforcement learning (RL) algorithm into a model-based approach, effectively decoupling physical environment modeling from RL algorithm development. This separation allows for independent advancements in RL algorithms and world modeling, significantly improving sample efficiency and enhancing generalization capabilities. Our findings highlight that world models for motion dynamics is a promising direction for developing more versatile and capable embodied systems.
Blind Bitstream-corrupted Video Recovery via Metadata-guided Diffusion Model
Authors: Shuyun Wang, Hu Zhang, Xin Shen, Dadong Wang, Xin Yu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    Bitstream-corrupted video recovery aims to fill in realistic video content due to bitstream corruption during video storage or transmission. Most existing methods typically assume that the predefined masks of the corrupted regions are known in advance. However, manually annotating these masks is laborious and time-consuming, limiting the applicability of existing methods in real-world scenarios. Therefore, we expect to relax this assumption by defining a new blind video recovery setting where the recovery of corrupted regions does not rely on predefined masks. There are two significant challenges in this setting: (i) without predefined masks, how accurately can a model identify the regions requiring recovery? (ii) how to recover contents from extensive and irregular regions, especially when large portions of frames are severely degraded? To address these challenges, we introduce a Metadata-Guided Diffusion Model, dubbed M-GDM. To enable a diffusion model focusing on the corrupted regions, we leverage intrinsic video metadata as a corruption indicator and design a dual-stream metadata encoder. This encoder first embeds the motion vectors and frame types of a video separately and then merges them into a unified metadata representation. The metadata representation will interact with the corrupted latent feature through cross-attention mechanisms at each diffusion step. Meanwhile, to preserve the intact regions, we propose a prior-driven mask predictor that generates pseudo masks for the corrupted regions by leveraging the metadata prior and diffusion prior. These pseudo masks enable the separation and recombination of intact and recovered regions through hard masking. However, imperfections in pseudo mask predictions and hard masking processes often result in boundary artifacts. Thus, we introduce a post-refinement module that refines the hard-masked outputs, enhancing the consistency between intact and recovered regions. Extensive experiment results validate the effectiveness of our method and demonstrate its superiority in the blind video recovery task.
Multi-modal Medical Diagnosis via Large-small Model Collaboration
Authors: Wanyi Chen, Zihua Zhao, Jiangchao Yao, Ya zhang, Jiajun Bu, Haishuai Wang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Diagnostic, Medical Diagnosis

    Recent advances in medical AI have shown a clear trend towards large models in healthcare. However, developing large models for multi-modal medical diagnosis remains challenging due to a lack of sufficient modal-complete medical data. Most existing multi-modal diagnostic models are relatively small and struggle with limited feature extraction capabilities. To bridge this gap, we propose **AdaCoMed**, an **ada**ptive **co**llaborative-learning framework that synergistically integrates the off-the-shelf **med**ical single-modal large models with multi-modal small models. Our framework first employs a mixture-of-modality-experts (MoME) architecture to combine features extracted from multiple single-modal medical large models, and then introduces a novel adaptive co-learning mechanism to collaborate with a multi-modal small model. This co-learning mechanism, guided by an adaptive weighting strategy, dynamically balances the complementary strengths between the MoME-fused large model features and the cross-modal reasoning capabilities of the small model. Extensive experiments on two representative multi-modal medical datasets (MIMIC-IV-MM and MMIST ccRCC) across six modalities and four diagnostic tasks demonstrate consistent improvements over state-of-the-art baselines, making it a promising solution for real-world medical diagnosis applications.
Multi-Modal Contrastive Masked Autoencoders: A Two-Stage Progressive Pre-training Approach for RGBD Datasets
Authors: Muhammad Abdullah Jamal, Omid Mohareri
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Contrastive Learning, Denoising, Semantic Segmentation

    In this paper, we propose a new progressive pre-training method for image understanding tasks which leverages RGB-D datasets. The method utilizes Multi-Modal Contrastive Masked Autoencoder and Denoising techniques. Our proposed approach consists of two stages. In the first stage, we pre-train the model using contrastive learning to learn cross-modal representations. In the second stage, we further pre-train the model using masked autoencoding and denoising/noise prediction used in diffusion models. Masked autoencoding focuses on reconstructing the missing patches in the input modality using local spatial correlations, while denoising learns high frequency components of the input data. Moreover, it incorporates global distillation in the second stage by leveraging the knowledge acquired in stage one. Our approach is scalable, robust and suitable for pre-training RGB-D datasets. Extensive experiments on multiple datasets such as ScanNet, NYUv2 and SUN RGB-D show the efficacy and superior performance of our approach. Specifically, we show an improvement of +1.3% mIoU against Mask3D on ScanNet semantic segmentation. We further demonstrate the effectiveness of our approach in low-data regime by evaluating it for semantic segmentation task against the state-of-the-art methods.
FedSPA: Generalizable Federated Graph Learning under Homophily Heterogeneity
Authors: Zihan Tan, Guancheng Wan, Wenke Huang, He Li, Guibin Zhang, Carl Yang, Mang Ye
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Graph Learning

    Federated Graph Learning (FGL) has emerged as a solution to address real-world privacy concerns and data silos in graph learning, which relies on Graph Neural Networks (GNNs). Nevertheless, the homophily level discrepancies within the local graph data of clients, termed homophily heterogeneity, significantly degrade the generalizability of a global GNN. Existing research ignores this issue and suffers from unpromising collaboration. In this paper, we propose FedSPA, an effective framework that addresses homophily heterogeneity from the perspectives of homophily conflict and homophily bias. In the first place, the homophily conflict arises when training on inconsistent homophily levels across clients. Correspondingly, we propose Subgraph Feature Propagation Decoupling (SFPD), thereby achieving collaboration on unified homophily levels across clients. To further address homophily bias, we design Homophily Bias-Driven Aggregation (HBDA) which emphasizes clients with lower biases. It enables the adaptive adjustment of each client contribution to the global GNN based on its homophily bias. The superiority of FedSPA is validated through extensive experiments. The code is available at https://github.com/OakleyTan/FedSPA.
FSHNet: Fully Sparse Hybrid Network for 3D Object Detection
Authors: Shuai Liu, Mingyue Cui, Boyang Li, Quanmin Liang, Tinghe Hong, Yunxiao Shan, Kai Huang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Object Detection, object-detection, Object Detection

    Fully sparse 3D detectors have recently gained significant attention due to their efficiency in long-range detection. However, sparse 3D detectors extract features only from non-empty voxels, which impairs long-range interactions and causes the center feature missing. The former weakens the feature extraction capability, while the latter hinders network optimization. To address these challenges, we introduce the Fully Sparse Hybrid Network (FSHNet). FSHNet incorporates a proposed SlotFormer block to enhance the long-range feature extraction capability of existing sparse encoders. The SlotFormer divides sparse voxels using a slot partition approach, which, compared to traditional window partition, provides a larger receptive field. Additionally, we propose a dynamic sparse label assignment strategy to deeply optimize the network by providing more high-quality positive samples. To further enhance performance, we introduce a sparse upsampling module to refine downsampled voxels, preserving fine-grained details crucial for detecting small objects. Extensive experiments on the Waymo, nuScenes, and Argoverse2 benchmarks demonstrate the effectiveness of FSHNet. The code will be publicly available.
Advancing Adversarial Robustness in GNeRFs: The IL2-NeRF Attack
Authors: Nicole Meng, Caleb Manicke, Ronak Sahu, Caiwen Ding, Yingjie Lao
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Reconstruction, Adversarial Attack, Adversarial Robustness, NeRF, Novel View Synthesis

    Generalizable Neural Radiance Fields (GNeRF) are recognized as one of the most promising techniques for novel view synthesis and 3D model generation in real-world applications. However, like other generative models in computer vision, ensuring their adversarial robustness against various threat models is essential for practical use. The pioneering work in this area, NeRFool, introduced a state-of-the-art attack that targets GNeRFs by manipulating source views before feature extraction, successfully disrupting the color and density results of the constructed views. Building on this foundation, we propose IL2-NeRF (Iterative L2 NeRF Attack), a novel adversarial attack method that explores a new threat model (in the L2 domain) for attacking GNeRFs.  We evaluated IL2-NeRF against two standard GNeRF models across three benchmark datasets, demonstrating similar performance compared to NeRFool, based on the same evaluation metrics proposed by NeRFool. Our results establish IL2-NeRF as the first adversarial method for GNeRFs under the L2 norm. We establish a foundational L2 threat model for future research, enabling direct performance comparisons while introducing a smoother, image-wide perturbation approach in Adversarial 3D Reconstruction. Our code is available at: https://github.com/The-NRC-SCAR-Group/IL2-NeRF
Adaptive Parameter Selection for Tuning Vision-Language Models
Authors: Yi Zhang, Yi-Xuan Deng, Meng-Hao Guo, Shi-Min Hu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Few-Shot Learning

    Vision-language models (VLMs) like CLIP have been widely used in various specific tasks.Parameter-efficient fine-tuning (PEFT) methods, such as prompt and adapter tuning,have become key techniques for adapting these models to specific domains.However, existing approaches rely on prior knowledgeto manually identify the locations requiring fine-tuning.Adaptively selecting which parameters in VLMs should be tuned remains unexplored. In this paper, we propose CLIP with Adaptive Selective Tuning (CLIP-AST), which can be used to automatically select critical parameters in VLMs for fine-tuning for specific tasks.It opportunely leveragesthe adaptive learning rate in the optimizer and improves model performance without extra parameter overhead. We conduct extensive experiments on 13 benchmarks, such as ImageNet, Food101, Flowers102, etc,with different settings, including few-shot learning, base-to-novel class generalization, and out-of-distribution. The results show that CLIP-AST consistently outperforms the original CLIP model as well as its variantsand achieves state-of-the-art (SOTA) performance in all cases. For example, with the 16-shot learning, CLIP-AST surpasses GraphAdapter and PromptSRC by 3.56% and 2.20% in average accuracy on 11 datasets, respectively.Code will be publicly available.
Self-Supervised Spatial Correspondence Across Modalities
Authors: Ayush Shrivastava, Andrew Owens
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Geometric Matching, Semantic correspondence

    We present a method for finding cross-modal space-time correspondences.  Given two images from different visual modalities, such as an RGB image and a depth map, our model identifies which pairs of pixels correspond to the same physical points in the scene. To solve this problem, we extend the contrastive random walk framework to simultaneously learn cycle-consistent feature representations for both cross-modal and intra-modal matching. The resulting model is simple and has no explicit photo-consistency assumptions. It can be trained entirely using unlabeled data, without the need for any spatially aligned multimodal image pairs. We evaluate our method on both geometric and semantic correspondence tasks. For geometric matching, we consider challenging tasks such as RGB-to-depth and RGB-to-thermal matching (and vice versa); for semantic matching, we evaluate on photo-sketch and cross-style image alignment. Our method achieves strong performance across all benchmarks.
GeoDepth: From Point-to-Depth to Plane-to-Depth Modeling for Self-Supervised Monocular Depth Estimation
Authors: Haifeng Wu, Shuhang Gu, Lixin Duan, Wen Li
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Depth Estimation, Monocular Depth Estimation

    Self-supervised monocular depth estimation has long been treated as a point-wise prediction problem, where the depth of each pixel is usually estimated independently. However, artifacts are often observed in the estimated depth map, e.g., depth values for points located in the same region may jump dramatically. To address this issue, we propose a novel self-supervised monocular depth estimation framework called GeoDepth, where we explore the intrinsic geometric representation in 3D scenes for producing accurate and continuous depth maps. In particular, we model the complex 3D scene as a collection of planes with varying sizes, where each plane is characterized by a unique set of parameters, namely planar normal (indicating plane orientation) and planar offset (defining the perpendicular distance from the camera center to the plane). Under this modeling, points in the same plane are enforced to share a unique representation and their depth variations related only to pixel coordinates, thus this geometric relationship can be exploited to regularize the depth variations of these points. To this end, we design a structured plane generation module that introduces spatio-temporal geometric cues and the plane uniqueness principle to recover the correct scene plane representation. In addition, we develop a depth discontinuity module to identify depth discontinuity regions and subsequently optimize them. Our experiments on the KITTI and NYUv2 datasets demonstrate that GeoDepth achieves state-of-the-art performance, with additional tests on Make3D and ScanNet validating its generalization capabilities.
D^3: Scaling Up Deepfake Detection by Learning from Discrepancy
Authors: Yongqi Yang, Zhihao Qian, Ye Zhu, Olga Russakovsky, Yu Wu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: DeepFake Detection, Face Swapping

    The boom of Generative AI brings opportunities entangled with risks and concerns. Existing literature emphasizes the generalization capability of deepfake detection on unseen generators, significantly promoting the detector's ability to identify more universal artifacts. This work seeks a step toward a universal deepfake detection system with better generalization and robustness. We do so by first scaling up the existing detection task setup from the one-generator to multiple-generators in training, during which we disclose two challenges presented in prior methodological designs and demonstrate the divergence of detectors' performance. Specifically, we reveal that the current methods tailored for training on one specific generator either struggle to learn comprehensive artifacts from multiple generators or sacrifice their fitting ability for seen generators (i.e., In-Domain (ID) performance) to exchange the generalization for unseen generators (i.e., Out-Of-Domain (OOD) performance). To tackle the above challenges, we propose our Discrepancy Deepfake Detector (D3) framework, whose core idea is to deconstruct the universal artifacts from multiple generators by introducing a parallel network branch that takes a distorted image feature as an extra discrepancy signal and supplements its original counterpart. Extensive scaled-up experiments demonstrate the effectiveness of D3, achieving 5.3% accuracy improvement in the OOD testing compared to the current SOTA methods while maintaining the ID performance. The source code will be updated in our GitHub repository: https://github.com/BigAandSmallq/D3.
AdMiT: Adaptive Multi-Source Tuning in Dynamic Environments
Authors: Xiangyu Chang, Fahim Faisal Niloy, Sk Miraj Ahmed, Srikanth V. Krishnamurthy, Basak Guler, Ananthram Swami, Samet Oymak, Amit Roy-Chowdhury
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Visual Prompt Tuning

    Incorporating transformer models into edge devices poses a significant challenge due to the computational demands of adapting these large models across diverse applications. Parameter-efficient tuning (PET) methods (e.g. LoRA, Adapter, Visual Prompt Tuning, etc.) allow for targeted adaptation by modifying only small parts of the transformer model. However, adapting to dynamic unlabeled target distributions at the test time remains complex. To address this, we introduce AdMiT: Adaptive Multi -Source Tuning in Dynamic Environments. AdMiT innovates by pre-training a set of PET modules, each optimized for different source distributions or tasks, and dynamically selecting and integrating a sparse subset of relevant modules when encountering a new, few-shot, unlabeled target distribution. This integration leverages Kernel Mean Embedding (KME)-based matching to align the target distribution with relevant source knowledge efficiently, without requiring additional routing networks or hyperparameter tuning. AdMiT achieves adaptation with a single inference step, making it particularly suitable for resource-constrained edge deployments. Furthermore, AdMiT preserves privacy by performing an adaptation locally on each edge device, without the need for data exchange. Our theoretical analysis establishes guarantees for AdMiT's generalization, while extensive benchmarks demonstrate that AdMiT consistently outperforms other PET methods across a range of tasks, achieving robust and efficient adaptation.
Diff2Flow: Training Flow Matching Models via Diffusion Model Alignment
Authors: Johannes Schusterbauer, Ming Gui, Frank Fundel, Björn Ommer
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    Diffusion models have revolutionized generative tasks through high-fidelity outputs, yet flow matching (FM) offers faster inference and empirical performance gains. However, current foundation FM models are computationally prohibitive for finetuning, while diffusion models like Stable Diffusion benefit from efficient architectures and ecosystem support. This work addresses the critical challenge of efficiently transferring knowledge from pre-trained diffusion models to flow matching. We propose Diff2Flow, a novel framework that systematically bridges diffusion and FM paradigms by rescaling timesteps, aligning interpolants, and deriving FM-compatible velocity fields from diffusion predictions. This alignment enables direct and efficient FM finetuning of diffusion priors with no extra computation overhead. Our experiments demonstrate that Diff2Flow outperforms naive FM and diffusion finetuning particularly under parameter-efficient constraints, while achieving superior or competitive performance across diverse downstream tasks compared to state-of-the-art methods.