HAvatar: High-fidelity Head Avatar via Facial Model Conditioned Neural
  Radiance Field
Xiaochen Zhao Lizhen Wang Jingxiang Sun Hongwen Zhang Jinli Suo Yebin Liu
The problem of modeling an animatable 3D human head avatar under light-weight
setups is of significant importance but has not been well solved. Existing 3D
representations either perform well in the realism of portrait images synthesis
or the accuracy of expression control, but not both. To address the problem, we
introduce a novel hybrid explicit-implicit 3D representation, Facial Model
Conditioned Neural Radiance Field, which integrates the expressiveness of NeRF
and the prior information from the parametric template. At the core of our
representation, a synthetic-renderings-based condition method is proposed to
fuse the prior information from the parametric model into the implicit field
without constraining its topological flexibility. Besides, based on the hybrid
representation, we properly overcome the inconsistent shape issue presented in
existing methods and improve the animation stability. Moreover, by adopting an
overall GAN-based architecture using an image-to-image translation network, we
achieve high-resolution, realistic and view-consistent synthesis of dynamic
head appearance. Experiments demonstrate that our method can achieve
state-of-the-art performance for 3D head avatar animation compared with
previous methods.