  Learning deformable 3D objects from 2D images is often an ill-posed problem.
Existing methods rely on explicit supervision to establish multi-view
correspondences, such as template shape models and keypoint annotations, which
restricts their applicability on objects "in the wild". A more natural way of
establishing correspondences is by watching videos of objects moving around. In
this paper, we present DOVE, a method that learns textured 3D models of
deformable object categories from monocular videos available online, without
keypoint, viewpoint or template shape supervision. By resolving
symmetry-induced pose ambiguities and leveraging temporal correspondences in
videos, the model automatically learns to factor out 3D shape, articulated pose
and texture from each individual RGB frame, and is ready for single-image
inference at test time. In the experiments, we show that existing methods fail
to learn sensible 3D shapes without additional keypoint or template
supervision, whereas our method produces temporally consistent 3D models, which
can be animated and rendered from arbitrary viewpoints.
