GANHead: Towards Generative Animatable Neural Head Avatars
This work addresses the problem of creating efficient digital avatars for applications in virtual reality and entertainment, representing an incremental improvement by integrating existing techniques for better control and realism.
The authors tackled the challenge of generating complete, realistic, and animatable head avatars by proposing GANHead, which combines explicit expression control with implicit rendering, achieving superior performance in head avatar generation and raw scan fitting compared to state-of-the-art methods.
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.