  Given an "in-the-wild" video of a person, we reconstruct an animatable model
of the person in the video. The output model can be rendered in any body pose
to any camera view, via the learned controls, without explicit 3D mesh
reconstruction. At the core of our method is a volumetric 3D human
representation reconstructed with a deep network trained on input video,
enabling novel pose/view synthesis. Our method is an advance over GAN-based
image-to-image translation since it allows image synthesis for any pose and
camera via the internal 3D representation, while at the same time it does not
require a pre-rigged model or ground truth meshes for training, as in
mesh-based learning. Experiments validate the design choices and yield results
on synthetic data and on real videos of diverse people performing unconstrained
activities (e.g. dancing or playing tennis). Finally, we demonstrate motion
re-targeting and bullet-time rendering with the learned models.
