  Learning geometry, motion, and appearance priors of object classes is
important for the solution of a large variety of computer vision problems.
While the majority of approaches has focused on static objects, dynamic
objects, especially with controllable articulation, are less explored. We
propose a novel approach for learning a representation of the geometry,
appearance, and motion of a class of articulated objects given only a set of
color images as input. In a self-supervised manner, our novel representation
learns shape, appearance, and articulation codes that enable independent
control of these semantic dimensions. Our model is trained end-to-end without
requiring any articulation annotations. Experiments show that our approach
performs well for different joint types, such as revolute and prismatic joints,
as well as different combinations of these joints. Compared to state of the art
that uses direct 3D supervision and does not output appearance, we recover more
faithful geometry and appearance from 2D observations only. In addition, our
representation enables a large variety of applications, such as few-shot
reconstruction, the generation of novel articulations, and novel
view-synthesis.
