  Photorealistic rendering of dynamic humans is an important ability for
telepresence systems, virtual shopping, synthetic data generation, and more.
Recently, neural rendering methods, which combine techniques from computer
graphics and machine learning, have created high-fidelity models of humans and
objects. Some of these methods do not produce results with high-enough fidelity
for driveable human models (Neural Volumes) whereas others have extremely long
rendering times (NeRF). We propose a novel compositional 3D representation that
combines the best of previous methods to produce both higher-resolution and
faster results. Our representation bridges the gap between discrete and
continuous volumetric representations by combining a coarse 3D-structure-aware
grid of animation codes with a continuous learned scene function that maps
every position and its corresponding local animation code to its view-dependent
emitted radiance and local volume density. Differentiable volume rendering is
employed to compute photo-realistic novel views of the human head and upper
body as well as to train our novel representation end-to-end using only 2D
supervision. In addition, we show that the learned dynamic radiance field can
be used to synthesize novel unseen expressions based on a global animation
code. Our approach achieves state-of-the-art results for synthesizing novel
views of dynamic human heads and the upper body.
