用物理模型提升单目视频人体运动估计的生物力学真实度
MonoMSK: Monocular 3D Musculoskeletal Dynamics Estimation
- 结合数据驱动与物理模拟，用骨骼肌肉模型同步估计动作与受力
- 在BML-MoVi等数据集上显著提升动作精度，首次实现单目受力估计
- 适合需要高保真生物力学分析的医疗、康复与运动研究场景
重建具有生物力学真实性的3D人体运动——同时恢复运动学（动作）与动力学（力与力矩）——是一项关键挑战。尽管标记系统受限于实验室且效率低，现有单目方法多采用简化的解剖不准确模型（如SMPL），忽略物理规律，从根本上限制了其生物力学保真度。本文提出MonoMSK，一种融合数据驱动学习与物理仿真混合框架，可从单目视频中估计具有生物力学真实性的3D人体运动。MonoMSK通过解剖精确的肌肉骨骼模型，联合恢复运动学（动作）与动力学（力与力矩）。该方法整合基于Transformer的逆向动力学与可微分的前向运动学及动力学层，由基于常微分方程（ODE）的仿真控制，构建物理约束的逆-正向循环，确保生物力学因果关系与物理合理性。引入新型前向-逆向一致性损失，进一步对齐运动重建与底层动力学推理。在BML-MoVi、BEDLAM和OpenCap数据集上的实验表明，MonoMSK在运动学精度上显著优于现有最先进方法，并首次实现单目条件下的精确动力学估计。
原文摘要 · Abstract (English)
Reconstructing biomechanically realistic 3D human motion - recovering both kinematics (motion) and kinetics (forces) - is a critical challenge. While marker-based systems are lab-bound and slow, popular monocular methods use oversimplified, anatomically inaccurate models (e.g., SMPL) and ignore physics, fundamentally limiting their biomechanical fidelity. In this work, we introduce MonoMSK, a hybrid framework that bridges data-driven learning and physics-based simulation for biomechanically realistic 3D human motion estimation from monocular video. MonoMSK jointly recovers both kinematics (motions) and kinetics (forces and torques) through an anatomically accurate musculoskeletal model. By integrating transformer-based inverse dynamics with differentiable forward kinematics and dynamics layers governed by ODE-based simulation, MonoMSK establishes a physics-regulated inverse-forward loop that enforces biomechanical causality and physical plausibility. A novel forward-inverse consistency loss further aligns motion reconstruction with the underlying kinetic reasoning. Experiments on BML-MoVi, BEDLAM, and OpenCap show that MonoMSK significantly outperforms state-of-the-art methods in kinematic accuracy, while for the first time enabling precise monocular kinetics estimation.
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