3D Kinematics Estimation from Video with a Biomechanical Model and Synthetic Training Data

Zhi-Yi Lin, Bofan Lyu, Judith Cueto Fernandez, Eline Van Der Kruk, Ajay Seth, Xucong Zhang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 1441-1450

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.