MonoMSK: 单目3D肌肉骨骼动力学估计
MonoMSK: Monocular 3D Musculoskeletal Dynamics Estimation
AI总结：
提出MonoMSK混合框架，结合数据驱动与物理仿真，从单目视频估计生物力学真实的3D人体运动（运动学与动力学），通过ODE仿真和逆动力学实现高精度运动与力估计。
摘要：
重建生物力学真实的3D人体运动——同时恢复运动学（运动）和动力学（力）——是一个关键挑战。基于标记的系统受限于实验室且速度慢，而流行的单目方法使用过度简化、解剖学不准确的模型（如SMPL）并忽略物理规律，从根本上限制了其生物力学保真度。在这项工作中，我们引入了MonoMSK，一个混合框架，它桥接了数据驱动学习和基于物理的仿真，用于从单目视频进行生物力学真实的3D人体运动估计。MonoMSK通过一个解剖学准确的肌肉骨骼模型，联合恢复运动学（运动）和动力学（力和力矩）。通过将基于变换器的逆动力学与由基于ODE的仿真控制的可微前向运动学和动力学层相结合，MonoMSK建立了一个物理调节的逆-前向循环，强制执行生物力学因果性和物理合理性。一种新颖的前向-逆一致性损失进一步将运动重建与潜在的动力学推理对齐。在BML-MoVi、BEDLAM和OpenCap上的实验表明，MonoMSK在运动学精度上显著优于最先进的方法，同时首次实现了精确的单目动力学估计。
英文摘要：
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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