SViMo: Synchronized Diffusion for Video and Motion Generation in Hand-object Interaction Scenarios

• URL: http://arxiv.org/abs/2506.02444v3
• Date: Thu, 05 Jun 2025 03:21:54 GMT
• Title: SViMo: Synchronized Diffusion for Video and Motion Generation in Hand-object Interaction Scenarios
• Authors: Lingwei Dang, Ruizhi Shao, Hongwen Zhang, Wei Min, Yebin Liu, Qingyao Wu,
• Abstract summary: Hand-Object Interaction (HOI) generation has significant application potential.<n>Current 3D HOI motion generation approaches heavily rely on predefined 3D object models and lab-captured motion data.<n>We propose a novel framework that combines visual priors and dynamic constraints within a synchronized diffusion process to generate the HOI video and motion simultaneously.
• Score: 48.09735396455107
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hand-Object Interaction (HOI) generation has significant application potential. However, current 3D HOI motion generation approaches heavily rely on predefined 3D object models and lab-captured motion data, limiting generalization capabilities. Meanwhile, HOI video generation methods prioritize pixel-level visual fidelity, often sacrificing physical plausibility. Recognizing that visual appearance and motion patterns share fundamental physical laws in the real world, we propose a novel framework that combines visual priors and dynamic constraints within a synchronized diffusion process to generate the HOI video and motion simultaneously. To integrate the heterogeneous semantics, appearance, and motion features, our method implements tri-modal adaptive modulation for feature aligning, coupled with 3D full-attention for modeling inter- and intra-modal dependencies. Furthermore, we introduce a vision-aware 3D interaction diffusion model that generates explicit 3D interaction sequences directly from the synchronized diffusion outputs, then feeds them back to establish a closed-loop feedback cycle. This architecture eliminates dependencies on predefined object models or explicit pose guidance while significantly enhancing video-motion consistency. Experimental results demonstrate our method's superiority over state-of-the-art approaches in generating high-fidelity, dynamically plausible HOI sequences, with notable generalization capabilities in unseen real-world scenarios. Project page at https://github.com/Droliven/SViMo_project.

Related papers

• Diffusion-based 3D Hand Motion Recovery with Intuitive Physics [29.784542628690794]
  We present a novel 3D hand motion recovery framework that enhances image-based reconstructions.<n>Our model captures the distribution of refined motion estimates conditioned on initial ones, generating improved sequences.<n>We identify valuable intuitive physics knowledge during hand-object interactions, including key motion states and their associated motion constraints.
  arXiv  Detail & Related papers  (2025-08-03T16:44:24Z)
• Learning Video Generation for Robotic Manipulation with Collaborative Trajectory Control [72.00655365269]
  We present RoboMaster, a novel framework that models inter-object dynamics through a collaborative trajectory formulation.<n>Unlike prior methods that decompose objects, our core is to decompose the interaction process into three sub-stages: pre-interaction, interaction, and post-interaction.<n>Our method outperforms existing approaches, establishing new state-of-the-art performance in trajectory-controlled video generation for robotic manipulation.
  arXiv  Detail & Related papers  (2025-06-02T17:57:06Z)
• MAGIC: Motion-Aware Generative Inference via Confidence-Guided LLM [14.522189177415724]
  MAGIC is a training-free framework for single-image physical property inference and dynamic generation.<n>Our framework generates motion-rich videos from a static image and closes the visual-to-physical gap through a confidence-driven feedback loop.<n> Experiments show that MAGIC outperforms existing physics-aware generative methods in inference accuracy and achieves greater temporal coherence.
  arXiv  Detail & Related papers  (2025-05-22T09:40:34Z)
• ReVision: High-Quality, Low-Cost Video Generation with Explicit 3D Physics Modeling for Complex Motion and Interaction [22.420752010237052]
  We introduce ReVision, a plug-and-play framework that explicitly integrates parameterized 3D physical knowledge into a conditional video generation model.<n>We validate the effectiveness of our approach on Stable Video Diffusion, where ReVision significantly improves motion fidelity and coherence.<n>Our results suggest that, by incorporating 3D physical knowledge, even a relatively small video diffusion model can generate complex motions and interactions with greater realism and controllability.
  arXiv  Detail & Related papers  (2025-04-30T17:59:56Z)
• VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models [71.9811050853964]
  VideoJAM is a novel framework that instills an effective motion prior to video generators.<n>VideoJAM achieves state-of-the-art performance in motion coherence.<n>These findings emphasize that appearance and motion can be complementary and, when effectively integrated, enhance both the visual quality and the coherence of video generation.
  arXiv  Detail & Related papers  (2025-02-04T17:07:10Z)
• InterDyn: Controllable Interactive Dynamics with Video Diffusion Models [50.38647583839384]
  We propose InterDyn, a framework that generates videos of interactive dynamics given an initial frame and a control signal encoding the motion of a driving object or actor.<n>Our key insight is that large video generation models can act as both neurals and implicit physics simulators'', having learned interactive dynamics from large-scale video data.
  arXiv  Detail & Related papers  (2024-12-16T13:57:02Z)
• EgoGaussian: Dynamic Scene Understanding from Egocentric Video with 3D Gaussian Splatting [95.44545809256473]
  EgoGaussian is a method capable of simultaneously reconstructing 3D scenes and dynamically tracking 3D object motion from RGB egocentric input alone. We show significant improvements in terms of both dynamic object and background reconstruction quality compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2024-06-28T10:39:36Z)
• TrackDiffusion: Tracklet-Conditioned Video Generation via Diffusion Models [75.20168902300166]
  We propose TrackDiffusion, a novel video generation framework affording fine-grained trajectory-conditioned motion control. A pivotal component of TrackDiffusion is the instance enhancer, which explicitly ensures inter-frame consistency of multiple objects. generated video sequences by our TrackDiffusion can be used as training data for visual perception models.
  arXiv  Detail & Related papers  (2023-12-01T15:24:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.