  We introduce PeeledHuman - a novel shape representation of the human body
that is robust to self-occlusions. PeeledHuman encodes the human body as a set
of Peeled Depth and RGB maps in 2D, obtained by performing ray-tracing on the
3D body model and extending each ray beyond its first intersection. This
formulation allows us to handle self-occlusions efficiently compared to other
representations. Given a monocular RGB image, we learn these Peeled maps in an
end-to-end generative adversarial fashion using our novel framework - PeelGAN.
We train PeelGAN using a 3D Chamfer loss and other 2D losses to generate
multiple depth values per-pixel and a corresponding RGB field per-vertex in a
dual-branch setup. In our simple non-parametric solution, the generated Peeled
Depth maps are back-projected to 3D space to obtain a complete textured 3D
shape. The corresponding RGB maps provide vertex-level texture details. We
compare our method with current parametric and non-parametric methods in 3D
reconstruction and find that we achieve state-of-the-art-results. We
demonstrate the effectiveness of our representation on publicly available BUFF
and MonoPerfCap datasets as well as loose clothing data collected by our
calibrated multi-Kinect setup.
