  To bring digital avatars into people's lives, it is highly demanded to
efficiently generate complete, realistic, and animatable head avatars. This
task is challenging, and it is difficult for existing methods to satisfy all
the requirements at once. To achieve these goals, we propose GANHead
(Generative Animatable Neural Head Avatar), a novel generative head model that
takes advantages of both the fine-grained control over the explicit expression
parameters and the realistic rendering results of implicit representations.
Specifically, GANHead represents coarse geometry, fine-gained details and
texture via three networks in canonical space to obtain the ability to generate
complete and realistic head avatars. To achieve flexible animation, we define
the deformation filed by standard linear blend skinning (LBS), with the learned
continuous pose and expression bases and LBS weights. This allows the avatars
to be directly animated by FLAME parameters and generalize well to unseen poses
and expressions. Compared to state-of-the-art (SOTA) methods, GANHead achieves
superior performance on head avatar generation and raw scan fitting.

Computer Vision and Pattern Recognition