  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
