: Animals from images. We learn an articulated, 3D, statistical shape model of animals using very little training data. We fit the shape and pose of the model to 2D image cues showing how it generalizes to previously unseen shapes. 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 partbased 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.
The SMPL model was extended to animals by Zuffi et al. REF , where the lack of motion capture data for animal subjects is cleverly overcome by building the model from 41 3D scans of toy figurines from five quadruped families in arbitrary poses.
3D Menagerie: Modeling the 3D Shape and Pose of Animals
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science