GaussianAvatar-Editor：逼真可动的高斯头部化身编辑器

GaussianAvatar-Editor: Photorealistic Animatable Gaussian Head Avatar Editor

January 17, 2025

作者: Xiangyue Liu, Kunming Luo, Heng Li, Qi Zhang, Yuan Liu, Li Yi, Ping Tan

cs.AI

摘要

我们介绍了GaussianAvatar-Editor，这是一个创新的框架，用于基于文本驱动的可编辑高斯头像化身，可以完全控制表情、姿势和视角。与静态3D高斯编辑不同，编辑可动画的4D高斯化身面临与动作遮挡和空间-时间不一致性相关的挑战。为了解决这些问题，我们提出了加权Alpha混合方程（WABE）。该函数增强了可见高斯的混合权重，同时抑制了对不可见高斯的影响，有效处理了编辑过程中的动作遮挡。此外，为了提高编辑质量并确保4D一致性，我们将条件对抗学习融入编辑过程中。这一策略有助于优化编辑结果并在整个动画过程中保持一致性。通过整合这些方法，我们的GaussianAvatar-Editor在可动画的4D高斯编辑中实现了逼真和一致的结果。我们在各种主题上进行了全面实验，验证了我们提出的技术的有效性，证明了我们方法的优越性。更多结果和代码请访问：[项目链接](https://xiangyueliu.github.io/GaussianAvatar-Editor/)。

English

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/).