GeneAvatar: Generic Expression-Aware Volumetric Head Avatar Editing from
  a Single Image
Chong Bao Yinda Zhang Yuan Li Xiyu Zhang Bangbang Yang Hujun Bao Marc Pollefeys Guofeng Zhang Zhaopeng Cui
Recently, we have witnessed the explosive growth of various volumetric
representations in modeling animatable head avatars. However, due to the
diversity of frameworks, there is no practical method to support high-level
applications like 3D head avatar editing across different representations. In
this paper, we propose a generic avatar editing approach that can be
universally applied to various 3DMM driving volumetric head avatars. To achieve
this goal, we design a novel expression-aware modification generative model,
which enables lift 2D editing from a single image to a consistent 3D
modification field. To ensure the effectiveness of the generative modification
process, we develop several techniques, including an expression-dependent
modification distillation scheme to draw knowledge from the large-scale head
avatar model and 2D facial texture editing tools, implicit latent space
guidance to enhance model convergence, and a segmentation-based loss reweight
strategy for fine-grained texture inversion. Extensive experiments demonstrate
that our method delivers high-quality and consistent results across multiple
expression and viewpoints. Project page: https://zju3dv.github.io/geneavatar/