  Although commercial and open-source software exist to reconstruct a static
object from a sequence recorded with an RGB-D sensor, there is a lack of tools
that build rigged models of articulated objects that deform realistically and
can be used for tracking or animation. In this work, we fill this gap and
propose a method that creates a fully rigged model of an articulated object
from depth data of a single sensor. To this end, we combine deformable mesh
tracking, motion segmentation based on spectral clustering and skeletonization
based on mean curvature flow. The fully rigged model then consists of a
watertight mesh, embedded skeleton, and skinning weights.
