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 all the meshes using an unsupervised
non-rigid technique [the CC algorithm] .
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 several real world
datasets, including scans of human arms, wooden puppets and entire
human bodies.
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