  We present FaceVerse, a fine-grained 3D Neural Face Model, which is built
from hybrid East Asian face datasets containing 60K fused RGB-D images and 2K
high-fidelity 3D head scan models. A novel coarse-to-fine structure is proposed
to take better advantage of our hybrid dataset. In the coarse module, we
generate a base parametric model from large-scale RGB-D images, which is able
to predict accurate rough 3D face models in different genders, ages, etc. Then
in the fine module, a conditional StyleGAN architecture trained with
high-fidelity scan models is introduced to enrich elaborate facial geometric
and texture details. Note that different from previous methods, our base and
detailed modules are both changeable, which enables an innovative application
of adjusting both the basic attributes and the facial details of 3D face
models. Furthermore, we propose a single-image fitting framework based on
differentiable rendering. Rich experiments show that our method outperforms the
state-of-the-art methods.
