  Neural Radiance Fields (NeRF) are compelling techniques for modeling dynamic
3D scenes from 2D image collections. These volumetric representations would be
well suited for synthesizing novel facial expressions but for two problems.
First, deformable NeRFs are object agnostic and model holistic movement of the
scene: they can replay how the motion changes over time, but they cannot alter
it in an interpretable way. Second, controllable volumetric representations
typically require either time-consuming manual annotations or 3D supervision to
provide semantic meaning to the scene. We propose a controllable neural
representation for face self-portraits (CoNFies), that solves both of these
problems within a common framework, and it can rely on automated processing. We
use automated facial action recognition (AFAR) to characterize facial
expressions as a combination of action units (AU) and their intensities. AUs
provide both the semantic locations and control labels for the system. CoNFies
outperformed competing methods for novel view and expression synthesis in terms
of visual and anatomic fidelity of expressions.
