Recovering Articulated Model Topology from Observed Rigid Motion

Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)

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Authors

Leonid Taycher, John Iii, Trevor Darrell

Abstract

Accurate representation of articulated motion is a challenging problem for machine perception. Several successful tracking algorithms have been developed that model human body as an articulated tree. We pro- pose a learning-based method for creating such articulated models from observations of multiple rigid motions. This paper is concerned with recovering topology of the articulated model, when the rigid motion of constituent segments is known. Our approach is based on ļ¬nding the Maximum Likelihood tree shaped factorization of the joint probability density function (PDF) of rigid segment motions. The topology of graph- ical model formed from this factorization corresponds to topology of the underlying articulated body. We demonstrate the performance of our al- gorithm on both synthetic and real motion capture data.