Journal
2013 INTERNATIONAL CONFERENCE ON 3D VISION (3DV 2013)
Volume -, Issue -, Pages 279-286Publisher
IEEE
DOI: 10.1109/3DV.2013.44
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Reconstructing a three-dimensional representation of human motion in real-time constitutes an important research topic with applications in sports sciences, human-computer-interaction, and the movie industry. In this paper, we contribute with a robust algorithm for estimating a personalized human body model from just two sequentially captured depth images that is more accurate and runs an order of magnitude faster than the current state-of-the-art procedure. Then, we employ the estimated body model to track the pose in real-time from a stream of depth images using a tracking algorithm that combines local pose optimization and a stabilizing database look-up. Together, this enables accurate pose tracking that is more accurate than previous approaches. As a further contribution, we evaluate and compare our algorithm to previous work on a comprehensive benchmark dataset containing more than 15 minutes of challenging motions. This dataset comprises calibrated marker-based motion capture data, depth data, as well as ground truth tracking results and is publicly available for research purposes.
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