  We present a neural network approach to transfer the motion from a single
image of an articulated object to a rest-state (i.e., unarticulated) 3D model.
Our network learns to predict the object's pose, part segmentation, and
corresponding motion parameters to reproduce the articulation shown in the
input image. The network is composed of three distinct branches that take a
shared joint image-shape embedding and is trained end-to-end. Unlike previous
methods, our approach is independent of the topology of the object and can work
with objects from arbitrary categories. Our method, trained with only synthetic
data, can be used to automatically animate a mesh, infer motion from real
images, and transfer articulation to functionally similar but geometrically
distinct 3D models at test time.
