Bayesian Estimation of 3-D Human Motion
We address the problem of reconstructing the 3-dimensional motions of a human gure from a monocular image sequence. We take a statistical approach, and use a set of motion capture examples to build a gaussian probability model for short human motion sequences. We rst study this model in a simpli ed rendering domain. This yields analytic results for the optimal 3-d estimate given a 2-d temporal sequence, as well as for which motion modes are di cult to estimate. The results from the simpli ed rendering conditions show that if we can overlay a stick gure on an image of a moving human, we can estimate his or her 3-d motion well. We built an interactive tracking system to process real video sequences, and can achieve good 3-d reconstructions of the human gure motion. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonpro t educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Information Technology Center America; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Information Technology Center America. All rights reserved. Copyright c Mitsubishi Electric Information Technology Center America, 1998 201 Broadway, Cambridge, Massachusetts 02139 1. First printing, TR98-05, April, 1998 1