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Online marker labeling for fully automatic skeleton tracking in optical motion capture

Methods to accurately capture the motion of humans in motion capture systems from optical markers are important for a large variety of applications including animation, interaction, orthopedics, and rehabilitation. Major challenges in this context are to associate the observed markers with skeleton segments, to track markers between consecutive frames, and to estimate the underlying skeleton configuration for each frame. Existing solutions to this problem often assume fully labeled markers, which usually requires labor-intensive manual labeling, especially when markers are temporally occluded during the movements. In this paper, we propose a fully automated method to initialize and track the skeleton configuration of humans from optical motion capture data without the need of any user intervention. Our method applies a flexible T-pose-based initialization that works with a wide range of marker placements, robustly estimates the skeleton configuration through least-squares optimization, and exploits the skeleton structure for fully automatic marker labeling. We demonstrate the capabilities of our approach for online skeleton tracking and show that our method outperforms solutions that are widely used and considered as state of the art.