Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction
This work addresses the problem of faithfully reproducing human faces for telepresence applications in AR/VR, offering an incremental improvement in quality.
This paper introduces dynamic neural radiance fields to model human face appearance and dynamics. It achieves photo-realistic image generation from monocular input, surpassing state-of-the-art video-based reenactment methods.
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.