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.
image motion analysis, image reconstruction, image sequences, object tracking, optimisation, pose estimation, 3D human motion reconstruction, database look-up, depth data, human-computer-interaction, local pose optimization, marker-based motion capture data, movie industry, personalized human body model, pose tracking, real-time depth-based full body tracker evaluation, real-time depth-based full body tracker personalization, sports sciences, Computational modeling, Estimation, Optimization, Shape, Three-dimensional displays, Tracking, Vectors, depth sensors, full-body motion tracking, human shape estimation