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3D-aware generative adversarial networks (GANs) synthesize high-fidelity andmulti-view-consistent facial images using only collections of single-view 2Dimagery. Towards fine-grained control over facial attributes, recent effortsincorporate 3D Morphable Face Model (3DMM) to describe deformation ingenerative radiance fields either explicitly or implicitly. Explicit methodsprovide fine-grained expression control but cannot handle topological changescaused by hair and accessories, while implicit ones can model varied topologiesbut 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. Toachieve both deformation accuracy and topological flexibility, we propose a 3Drepresentation called Generative Texture-Rasterized Tri-planes. The proposedrepresentation learns Generative Neural Textures on top of parametric meshtemplates and then projects them into three orthogonal-viewed feature planesthrough rasterization, forming a tri-plane feature representation for volumerendering. In this way, we combine both fine-grained expression control ofmesh-guided explicit deformation and the flexibility of implicit volumetricrepresentation. We further propose specific modules for modeling mouth interiorwhich is not taken into account by 3DMM. Our method demonstratesstate-of-the-art 3D-aware synthesis quality and animation ability throughextensive experiments. Furthermore, serving as 3D prior, our animatable 3Drepresentation boosts multiple applications including one-shot facial avatarsand 3D-aware stylization.

Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatars