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arXiv · 2505.01083

DexFlow: A Unified Approach for Dexterous Hand Pose Retargeting and Interaction

Xiaoyi Lin·Kunpeng Yao·Lixin Xu·Xueqiang Wang·Xuetao Li·Yuchen Wang·Miao Li
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Abstract

Despite advances in hand-object interaction modeling, generating realistic dexterous manipulation data for robotic hands remains a challenge. Retargeting methods often suffer from low accuracy and fail to account for hand-object interactions, leading to artifacts like interpenetration. Generative methods, lacking human hand priors, produce limited and unnatural poses. We propose a data transformation pipeline that combines human hand and object data from multiple sources for high-precision retargeting. Our approach uses a differential loss constraint to ensure temporal consistency and generates contact maps to refine hand-object interactions. Experiments show our method significantly improves pose accuracy, naturalness, and diversity, providing a robust solution for hand-object interaction modeling.

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Xiaoyi Lin, Kunpeng Yao, Lixin Xu, Xueqiang Wang, Xuetao Li, Yuchen Wang, Miao Li. 2025-05-02. DexFlow: A Unified Approach for Dexterous Hand Pose Retargeting and Interaction. https://arxiv.org/abs/2505.01083

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