Benchmarking raw datasets and collaboratively-evolving processed data for markerless motion capture analysis

We present a dataset designed for benchmarking markerless motion capture methods (from videos to joint kinematics). The dataset includes both raw and processed data. Two participants performed five tasks - walking, sit-to-stand, manual material handling, handstand hold or Y-pose (depending on the participant), and a jointly performed dance sequence. Movements were captured simultaneously recorded using 10 optoelectronic cameras (Qualisys Miqus M3, 120 Hz) and 9 video cameras (Qualisys Miqus Video, 60 Hz, 1920×1088 pixels). The raw dataset provides 3D marker trajectories and video recordings. The processed dataset includes joint kinematics obtained from both marker-based motion capture and 7 different markerless methods, contributed by multiple research teams as part of a challenge organized during a national biomechanics seminar. Additionally, the open-access GitHub repository containing processed data enables researchers to contribute new markerless methods estimated and expand the dataset collaboratively. This resource aims to facilitate benchmarking and support the development of robust markerless motion analysis methods.