  There has been significant work on learning realistic, articulated, 3D models
of the human body. In contrast, there are few such models of animals, despite
many applications. The main challenge is that animals are much less cooperative
than humans. The best human body models are learned from thousands of 3D scans
of people in specific poses, which is infeasible with live animals.
Consequently, we learn our model from a small set of 3D scans of toy figurines
in arbitrary poses. We employ a novel part-based shape model to compute an
initial registration to the scans. We then normalize their pose, learn a
statistical shape model, and refine the registrations and the model together.
In this way, we accurately align animal scans from different quadruped families
with very different shapes and poses. With the registration to a common
template we learn a shape space representing animals including lions, cats,
dogs, horses, cows and hippos. Animal shapes can be sampled from the model,
posed, animated, and fit to data. We demonstrate generalization by fitting it
to images of real animals including species not seen in training.
