GaussianAvatar-Editor: Photorealistic Animatable Gaussian Head Avatar
  Editor
Authors: name: Xiangyue Liu, name: Kunming Luo, name: Heng Li, name: Qi Zhang, name: Yuan Liu, name: Li Yi, name: Ping Tan

We introduce GaussianAvatar-Editor, an innovative framework for text-driven
editing of animatable Gaussian head avatars that can be fully controlled in
expression, pose, and viewpoint. Unlike static 3D Gaussian editing, editing
animatable 4D Gaussian avatars presents challenges related to motion occlusion
and spatial-temporal inconsistency. To address these issues, we propose the
Weighted Alpha Blending Equation (WABE). This function enhances the blending
weight of visible Gaussians while suppressing the influence on non-visible
Gaussians, effectively handling motion occlusion during editing. Furthermore,
to improve editing quality and ensure 4D consistency, we incorporate
conditional adversarial learning into the editing process. This strategy helps
to refine the edited results and maintain consistency throughout the animation.
By integrating these methods, our GaussianAvatar-Editor achieves photorealistic
and consistent results in animatable 4D Gaussian editing. We conduct
comprehensive experiments across various subjects to validate the effectiveness
of our proposed techniques, which demonstrates the superiority of our approach
over existing methods. More results and code are available at: [Project
Link](https://xiangyueliu.github.io/GaussianAvatar-Editor/).