3D Menagerie: Modeling the 3D shape and pose of animals
This work addresses the scarcity of articulated 3D animal models for applications in graphics and vision, representing an incremental advance over existing human-focused methods.
The paper tackles the problem of creating realistic 3D models of animals, which is challenging due to the lack of cooperative data, by learning from a small set of 3D scans of toy figurines and achieving accurate alignment and generalization to unseen species.
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.