  We present Neural Head Avatars, a novel neural representation that explicitly
models the surface geometry and appearance of an animatable human avatar that
can be used for teleconferencing in AR/VR or other applications in the movie or
games industry that rely on a digital human. Our representation can be learned
from a monocular RGB portrait video that features a range of different
expressions and views. Specifically, we propose a hybrid representation
consisting of a morphable model for the coarse shape and expressions of the
face, and two feed-forward networks, predicting vertex offsets of the
underlying mesh as well as a view- and expression-dependent texture. We
demonstrate that this representation is able to accurately extrapolate to
unseen poses and view points, and generates natural expressions while providing
sharp texture details. Compared to previous works on head avatars, our method
provides a disentangled shape and appearance model of the complete human head
(including hair) that is compatible with the standard graphics pipeline.
Moreover, it quantitatively and qualitatively outperforms current state of the
art in terms of reconstruction quality and novel-view synthesis.
