  We present dynamic neural radiance fields for modeling the appearance and
dynamics of a human face. Digitally modeling and reconstructing a talking human
is a key building-block for a variety of applications. Especially, for
telepresence applications in AR or VR, a faithful reproduction of the
appearance including novel viewpoints or head-poses is required. In contrast to
state-of-the-art approaches that model the geometry and material properties
explicitly, or are purely image-based, we introduce an implicit representation
of the head based on scene representation networks. To handle the dynamics of
the face, we combine our scene representation network with a low-dimensional
morphable model which provides explicit control over pose and expressions. We
use volumetric rendering to generate images from this hybrid representation and
demonstrate that such a dynamic neural scene representation can be learned from
monocular input data only, without the need of a specialized capture setup. In
our experiments, we show that this learned volumetric representation allows for
photo-realistic image generation that surpasses the quality of state-of-the-art
video-based reenactment methods.
