Markerless Motion Capture in the Crowd
This addresses the challenge of markerless motion capture for applications like animation or biomechanics, but appears incremental as it builds on existing crowdsourcing and reconstruction methods.
The paper tackles the problem of obtaining motion capture data from video by using crowdsourced workers to annotate body configurations in 2D, then reconstructing 3D structure, but does not provide concrete numerical results.
This work uses crowdsourcing to obtain motion capture data from video recordings. The data is obtained by information workers who click repeatedly to indicate body configurations in the frames of a video, resulting in a model of 2D structure over time. We discuss techniques to optimize the tracking task and strategies for maximizing accuracy and efficiency. We show visualizations of a variety of motions captured with our pipeline then apply reconstruction techniques to derive 3D structure.