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: this https URL

我们提出了一种通用的头像编辑方法，可以在各种三维头像模型下进行二维编辑，通过设计表情感知的修改生成模型，利用多种技术手段实现了高质量和一致性的结果。