用文本控制生成可动、逼真的3D头像，解决动作遮挡和时空不一致问题。
GaussianAvatar-Editor: Photorealistic Animatable Gaussian Head Avatar Editor
- 通过加权透明度融合提升可见点云权重，抑制不可见点影响。
- 引入条件对抗学习，确保动画过程中的4D一致性与高质量输出。
- 支持表情、姿态、视角全控，适合虚拟人、数字主播等应用。
我们提出GaussianAvatar-Editor，一种面向可动画4D高斯头像的文本驱动编辑框架，实现对表情、姿态和视角的完全控制。与静态3D高斯编辑不同，可动画4D高斯编辑面临运动遮挡和时空不一致的挑战。为此，我们提出加权透明度融合方程（WABE），增强可见高斯点的融合权重，同时抑制非可见点的影响，有效处理编辑中的运动遮挡问题。为进一步提升编辑质量并保障4D一致性，我们在编辑过程中引入条件对抗学习策略，以优化结果并维持动画连续性。在多个受试者上的综合实验验证了所提方法的有效性，结果表明该方法优于现有技术。更多结果与代码详见项目主页：[Project Link](https://xiangyueliu.github.io/GaussianAvatar-Editor/)。
原文摘要 · Abstract (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/).
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