标题： SViMo：手部与物体交互场景下用于视频和运动生成的同步扩散模型 显示英文标题

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

摘要： 手-物交互（HOI）生成具有重要的应用潜力。然而，当前的3D HOI运动生成方法严重依赖预定义的3D物体模型和实验室捕获的运动数据，限制了其泛化能力。同时，HOI视频生成方法优先考虑像素级视觉保真度，往往牺牲了物理上的合理性。 认识到视觉外观和运动模式在现实世界中共享基本的物理规律，我们提出了一种新颖的框架，在同步扩散过程中结合视觉先验和动态约束，以同时生成HOI视频和运动。为了整合异构语义、外观和运动特征，我们的方法实现了三模态自适应调制来对齐特征，并结合3D全注意力来建模跨模态和模态内的依赖关系。 此外，我们引入了一个视觉感知的3D交互扩散模型，该模型直接从同步扩散输出生成显式的3D交互序列，然后将其反馈回来建立闭环反馈循环。这种架构消除了对手动物体模型或显式姿态指导的依赖，同时显著提高了视频与运动的一致性。 实验结果表明，我们的方法在生成高保真、动态合理的HOI序列方面优于最先进的方法，并且在未见过的真实场景中表现出显著的泛化能力。 项目页面见 https://github.com/Droliven/SViMo_project。 显示英文摘要

摘要： 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.