  Acquisition and rendering of photo-realistic human heads is a highly
challenging research problem of particular importance for virtual telepresence.
Currently, the highest quality is achieved by volumetric approaches trained in
a person specific manner on multi-view data. These models better represent fine
structure, such as hair, compared to simpler mesh-based models. Volumetric
models typically employ a global code to represent facial expressions, such
that they can be driven by a small set of animation parameters. While such
architectures achieve impressive rendering quality, they can not easily be
extended to the multi-identity setting. In this paper, we devise a novel
approach for predicting volumetric avatars of the human head given just a small
number of inputs. We enable generalization across identities by a novel
parameterization that combines neural radiance fields with local, pixel-aligned
features extracted directly from the inputs, thus sidestepping the need for
very deep or complex networks. Our approach is trained in an end-to-end manner
solely based on a photometric re-rendering loss without requiring explicit 3D
supervision.We demonstrate that our approach outperforms the existing state of
the art in terms of quality and is able to generate faithful facial expressions
in a multi-identity setting.
