  This paper presents an algorithm to reconstruct temporally consistent 3D
meshes of deformable object instances from videos in the wild. Without
requiring annotations of 3D mesh, 2D keypoints, or camera pose for each video
frame, we pose video-based reconstruction as a self-supervised online
adaptation problem applied to any incoming test video. We first learn a
category-specific 3D reconstruction model from a collection of single-view
images of the same category that jointly predicts the shape, texture, and
camera pose of an image. Then, at inference time, we adapt the model to a test
video over time using self-supervised regularization terms that exploit
temporal consistency of an object instance to enforce that all reconstructed
meshes share a common texture map, a base shape, as well as parts. We
demonstrate that our algorithm recovers temporally consistent and reliable 3D
structures from videos of non-rigid objects including those of animals captured
in the wild -- an extremely challenging task rarely addressed before.
