Learning Articulated Skeletons from Motion

Humans demonstrate a remarkable ability to parse complicated motion sequences into their constituent structures and motions. We investigate this problem, attempting to learn the structure of one or more articulated objects, given a time-series of feature positions. We model the observed sequence in terms of “stick figure” objects, under the assumption that the relative joint angles between sticks can change over time, but their lengths and connectivities are fixed. We formulate the problem in a single probabilistic model that includes multiple sub-components: associating the features with particular sticks, determining the proper number of sticks, and finding which sticks are physically joined. We test the algorithm on challenging 2D and 3D datasets including optical human motion capture and video of walking giraffes.