  3D-aware generative adversarial networks (GANs) synthesize high-fidelity and
multi-view-consistent facial images using only collections of single-view 2D
imagery. Towards fine-grained control over facial attributes, recent efforts
incorporate 3D Morphable Face Model (3DMM) to describe deformation in
generative radiance fields either explicitly or implicitly. Explicit methods
provide fine-grained expression control but cannot handle topological changes
caused by hair and accessories, while implicit ones can model varied topologies
but have limited generalization caused by the unconstrained deformation fields.
We propose a novel 3D GAN framework for unsupervised learning of generative,
high-quality and 3D-consistent facial avatars from unstructured 2D images. To
achieve both deformation accuracy and topological flexibility, we propose a 3D
representation called Generative Texture-Rasterized Tri-planes. The proposed
representation learns Generative Neural Textures on top of parametric mesh
templates and then projects them into three orthogonal-viewed feature planes
through rasterization, forming a tri-plane feature representation for volume
rendering. In this way, we combine both fine-grained expression control of
mesh-guided explicit deformation and the flexibility of implicit volumetric
representation. We further propose specific modules for modeling mouth interior
which is not taken into account by 3DMM. Our method demonstrates
state-of-the-art 3D-aware synthesis quality and animation ability through
extensive experiments. Furthermore, serving as 3D prior, our animatable 3D
representation boosts multiple applications including one-shot facial avatars
and 3D-aware stylization.
