Local pose prior based 3D human motion capture from depth camera

This paper introduces a local pose prior based real-time online approach to capture 3D human animation from a single depth camera. The key idea is to learn a series of local pose prior models with K motion capture examples from a pre-established large and heterogeneous human motion database, based on automatically extracted labelled virtual sparse 3D markers from captured depth image. Then, by solving a Maximum A Posterior (MAP) problem via an iteratively optimization process, the system automatically tracks the 3D human motion sequence. The experiments show that the proposed approach robustly captures the accurate 3D human motions at 25fps. The proposed tracking system can easily applied to different actors with large different body sizes via an automatically individual body parameters calibration process. The proposed system can widely apply to 3D game/movie produce, human-machine interaction. © Copyright 2016, Institute of Software, the Chinese Academy of Sciences. All rights reserved.