GaussianAvatar-Editor: Photorealistic Animatable Gaussian Head Avatar
  Editor
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/).
678dcddd1c0169c0bc73364e
Xiangyue Liu
claimed_verified
2025-01-21T13:10:10.397Z
64edbc604f6a405a135ee506
/avatars/9f4976d8bc093cb8c5604b7235e980e4.svg
Xiangyue Liu
user
Liuxy-cx
678dcddd1c0169c0bc73364f
Kunming Luo
admin_assigned
2025-01-20T14:37:28.443Z
64baa2a530a1f0f0f03e758c
/avatars/100deaba962f49520a028c94b51720b1.svg
Kunming Luo
user
coolbeam
678dcddd1c0169c0bc733650
Heng Li
678dcddd1c0169c0bc733651
Qi Zhang
claimed_verified
2025-01-21T10:07:10.035Z
65001a3632d2159207e9af77
/avatars/4b7bca2bdc9216fc06857dc1c2183ae6.svg
Qiqi Meng
user
mayushii
678dcddd1c0169c0bc733652
Yuan Liu
678dcddd1c0169c0bc733653
Li Yi
678dcddd1c0169c0bc733654
Ping Tan
GaussianAvatar-Editor: Photorealistic Animatable Gaussian Head Avatar
  Editor
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/).
GaussianAvatar-Editor uses weighted alpha blending and conditional adversarial learning to achieve photorealistic and consistent editing of animatable Gaussian head avatars.
GaussianAvatar-Editor Weighted Alpha Blending Equation WABE motion occlusion spatial-temporal inconsistency conditional adversarial learning photorealistic consistent editing