{"ID":2831600,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.07359","arxiv_id":"2512.07359","title":"Multi-Rigid-Body Approximation of Human Hands with Application to Digital Twin","abstract":"Human hand simulation plays a critical role in digital twin applications, requiring models that balance anatomical fidelity with computational efficiency. We present a complete pipeline for constructing multi-rigid-body approximations of human hands that preserve realistic appearance while enabling real-time physics simulation. Starting from optical motion capture of a specific human hand, we construct a personalized MANO (Multi-Abstracted hand model with Neural Operations) model and convert it to a URDF (Unified Robot Description Format) representation with anatomically consistent joint axes. The key technical challenge is projecting MANO's unconstrained SO(3) joint rotations onto the kinematically constrained joints of the rigid-body model. We derive closed-form solutions for single degree-of-freedom joints and introduce a Baker-Campbell-Hausdorff (BCH)-corrected iterative method for two degree-of-freedom joints that properly handles the non-commutativity of rotations. We validate our approach through digital twin experiments where reinforcement learning policies control the multi-rigid-body hand to replay captured human demonstrations. Quantitative evaluation shows sub-centimeter reconstruction error and successful grasp execution across diverse manipulation tasks.","short_abstract":"Human hand simulation plays a critical role in digital twin applications, requiring models that balance anatomical fidelity with computational efficiency. We present a complete pipeline for constructing multi-rigid-body approximations of human hands that preserve realistic appearance while enabling real-time physics si...","url_abs":"https://arxiv.org/abs/2512.07359","url_pdf":"https://arxiv.org/pdf/2512.07359v1","authors":"[\"Bin Zhao\",\"Yiwen Lu\",\"Haohua Zhu\",\"Xiao Li\",\"Sheng Yi\"]","published":"2025-12-08T09:59:41Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.GR\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}