发表机构
Pohang University of Science and Technology (POSTECH)（浦项科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何将人类物体交互演示重定向到物理模拟，提出WristMimic框架，分离无接触身体运动与手部操作，通过手腕引导，让手指从物体跟踪和接触结果学习行为，实验显示该框架在跨手部模型重定向时效果良好。
AI 中文摘要
将人类物体交互演示重新定向到基于物理的模拟，不仅需要再现身体运动，还需再现物体运动和使操作成功所需的接触情况。仅位置的手部轨迹无法指明操作物体所需的接触力，直接跟踪会过度约束富含接触的手指行为。我们引入了WristMimic，这是一个手腕引导的全身控制框架，明确将无接触的身体运动与富含接触的手部操作分离。无接触的身体和手腕由运动学姿态目标引导，手指并非直接由人类手部姿态监督，而是从物体跟踪和接触结果中学习抓握和操作行为。我们的关键见解是手腕是这两种状态之间的自然通道。它基本无接触且可通过运动学方式跟踪，同时能确定全局手部配置并使手指处于可触及的抓握范围内。为确保交互过程中手腕位置可靠，我们引入了特定于手腕的重置约束和奖励优先级。实验表明，WristMimic在实现手指不可知的跨不同手部模型重定向的同时，与使用全手指姿态监督的方法相当或更优。
英文摘要
Retargeting human object interaction demonstrations to physics based simulation requires reproducing not only body motion but also the object motion and contacts that make manipulation succeed. However, position only hand trajectories do not specify the contact forces needed to manipulate objects, and directly tracking them can overconstrain contact rich finger behavior. We introduce WristMimic, a wrist guided whole body control framework that explicitly separates contact free body motion from contact rich hand manipulation. The contact free body and wrist are guided by kinematic pose targets, whereas the fingers are not directly supervised by human hand pose. Instead, they learn grasping and manipulation behaviors from object tracking and contact outcomes. Our key insight is that the wrist is the natural gate between these two regimes. It is largely free from contact and can be tracked kinematically, yet it determines the global hand configuration and places the fingers within reachable grasp affordances. To ensure reliable wrist placement during interaction, we introduce wrist specific reset constraints and reward prioritization. Experiments show that WristMimic matches or surpasses methods using full finger pose supervision while enabling finger agnostic retargeting across diverse hand embodiments.
CommentsAccepted to ECCV 2026
