  We propose a novel unsupervised method to learn the pose and
part-segmentation of articulated objects with rigid parts. Given two
observations of an object in different articulation states, our method learns
the geometry and appearance of object parts by using an implicit model from the
first observation, distils the part segmentation and articulation from the
second observation while rendering the latter observation. Additionally, to
tackle the complexities in the joint optimization of part segmentation and
articulation, we propose a voxel grid-based initialization strategy and a
decoupled optimization procedure. Compared to the prior unsupervised work, our
model obtains significantly better performance, and generalizes to objects with
multiple parts while it can be efficiently from few views for the latter
observation.
