融合多源数据提升机器人手部操作的精准与自然度。
DexFlow: A Unified Approach for Dexterous Hand Pose Retargeting and Interaction
- 整合多源人手与物体数据，构建统一生成流程。
- 通过微分损失和接触图，显著降低穿模且提升动作连贯性。
- 适合需要真实手物交互模拟的机器人研发人员。
尽管手-物交互建模取得进展，为机器人手生成逼真的灵巧操作数据仍具挑战。现有重定向方法常因精度低且忽略手物交互，导致穿模等伪影；生成方法因缺乏人类手部先验，动作有限且不自然。本文提出一种数据转换流水线，融合多个来源的人手与物体数据，实现高精度重定向。采用微分损失约束保证时序一致性，并生成接触图以优化手物交互。实验表明，该方法显著提升姿态准确率、自然度与多样性，为手物交互建模提供稳健解决方案。
原文摘要 · Abstract (English)
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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