Accurate 3D kinematics estimation of human body is crucial in various applications for human health and mobility, such as rehabilitation, injury prevention, and diagnosis, as it helps to understand the biomechanical loading experienced during movement. Conventional marker-based motion capture is expensive in terms of financial investment, time, and the expertise required. Moreover, due to the scarcity of datasets with accurate annotations, existing markerless motion capture methods suffer from challenges including unreliable 2D keypoint detection, limited anatomic accuracy, and low generalization capability. In this work, we propose a novel biomechanics-aware network that directly outputs 3D kinematics from two input views with consideration of biomechanical prior and spatio-temporal information. To train the model, we create synthetic dataset ODAH with accurate kinematics annotations generated by aligning the body mesh from the SMPL-X model and a full-body OpenSim skeletal model. Our extensive experiments demonstrate that the proposed approach, only trained on synthetic data, outperforms previous state-of-the-art methods when evaluated across multiple datasets, revealing a promising direction for enhancing video-based human motion capture

翻译：人体精确的三维运动学估计在康复、损伤预防及诊断等关注人类健康与运动能力的各类应用中至关重要，因为这有助于理解运动过程中承受的生物力学负荷。传统基于标记点的运动捕捉在资金投入、时间成本和专业技能要求方面代价高昂。此外，由于缺乏精确标注的数据集，现有无标记运动捕捉方法面临二维关键点检测不可靠、解剖学精度受限及泛化能力不足等挑战。本文提出一种新型生物力学感知网络，该网络考虑生物力学先验及时空信息，可直接从两个输入视角输出三维运动学参数。为训练模型，我们通过将SMPL-X模型的身体网格与全身OpenSim骨骼模型对齐，创建了带有精确运动学标注的合成数据集ODAH。大量实验证明，仅依靠合成数据训练的所提方法在多个数据集上的评估结果均优于现有最先进方法，这为增强基于视频的人体运动捕捉开辟了有前景的研究方向。