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.

使用动态神经辐射场模型重新构建并生成能够通过单目输入数据学习的场景表示，结合场景表示网络和低维可变形模型进行虚拟场景生成。