  We present a system for learning full-body neural avatars, i.e. deep networks
that produce full-body renderings of a person for varying body pose and camera
position. Our system takes the middle path between the classical graphics
pipeline and the recent deep learning approaches that generate images of humans
using image-to-image translation. In particular, our system estimates an
explicit two-dimensional texture map of the model surface. At the same time, it
abstains from explicit shape modeling in 3D. Instead, at test time, the system
uses a fully-convolutional network to directly map the configuration of body
feature points w.r.t. the camera to the 2D texture coordinates of individual
pixels in the image frame. We show that such a system is capable of learning to
generate realistic renderings while being trained on videos annotated with 3D
poses and foreground masks. We also demonstrate that maintaining an explicit
texture representation helps our system to achieve better generalization
compared to systems that use direct image-to-image translation.
