GeneAvatar: Generic Expression-Aware Volumetric Head Avatar Editing from a Single Image
This addresses the challenge of practical 3D head avatar editing for applications in animation and virtual reality, though it is incremental as it builds on existing volumetric representations and editing tools.
The paper tackles the problem of editing 3D head avatars across diverse volumetric representations by proposing a generic approach that lifts 2D edits from a single image to a consistent 3D modification field, achieving high-quality and consistent results across multiple expressions and viewpoints.
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/