PersonalizationandEvaluationofaReal-timeDepth-basedFullBodyTracker

Abstract

Reconstructing a three-dimensional representation of human motion in real-time constitutes an important research topic with applications in sports sciences, humancomputer-interaction,andthemovieindustry.Inthispaper, 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-ofthe-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 lookup. Together, this enables accurate pose tracking that is more accurate than previous approaches. As a further contribution, we evaluate and compare our algorithm t