  We address the problem of unsupervised learning of complex articulated object
models from 3D range data. We describe an algorithm whose input is a set of
meshes corresponding to different configurations of an articulated object. The
algorithm automatically recovers a decomposition of the object into
approximately rigid parts, the location of the parts in the different object
instances, and the articulated object skeleton linking the parts. Our algorithm
first registers allthe meshes using an unsupervised non-rigid technique
described in a companion paper. It then segments the meshes using a graphical
model that captures the spatial contiguity of parts. The segmentation is done
using the EM algorithm, iterating between finding a decomposition of the object
into rigid parts, and finding the location of the parts in the object
instances. Although the graphical model is densely connected, the object
decomposition step can be performed optimally and efficiently, allowing us to
identify a large number of object parts while avoiding local maxima. We
demonstrate the algorithm on real world datasets, recovering a 15-part
articulated model of a human puppet from just 7 different puppet
configurations, as well as a 4 part model of a fiexing arm where significant
non-rigid deformation was present.
