  Acquisition and creation of digital human avatars is an important problem
with applications to virtual telepresence, gaming, and human modeling. Most
contemporary approaches for avatar generation can be viewed either as 3D-based
methods, which use multi-view data to learn a 3D representation with appearance
(such as a mesh, implicit surface, or volume), or 2D-based methods which learn
photo-realistic renderings of avatars but lack accurate 3D representations. In
this work, we present, DRaCoN, a framework for learning full-body volumetric
avatars which exploits the advantages of both the 2D and 3D neural rendering
techniques. It consists of a Differentiable Rasterization module, DiffRas, that
synthesizes a low-resolution version of the target image along with additional
latent features guided by a parametric body model. The output of DiffRas is
then used as conditioning to our conditional neural 3D representation module
(c-NeRF) which generates the final high-res image along with body geometry
using volumetric rendering. While DiffRas helps in obtaining photo-realistic
image quality, c-NeRF, which employs signed distance fields (SDF) for 3D
representations, helps to obtain fine 3D geometric details. Experiments on the
challenging ZJU-MoCap and Human3.6M datasets indicate that DRaCoN outperforms
state-of-the-art methods both in terms of error metrics and visual quality.
