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.
我们提出了 GaussianAvatar-Editor，这是一个用于文本驱动可动画高斯头部头像编辑的创新框架，可实现对表情、姿态和视角的全面控制。与静态 3D 高斯编辑不同，编辑可动画的 4D 高斯头像面临运动遮挡和时空一致性的问题。 为了解决这些挑战，我们提出了加权阿尔法混合方程（Weighted Alpha Blending Equation, WABE）。该函数通过增强可见高斯的混合权重，同时抑制对不可见高斯的影响，能够有效处理编辑过程中出现的运动遮挡。此外，为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。此策略有助于优化编辑结果，并在动画的整个过程中保持一致性。 通过整合这些方法，我们的 GaussianAvatar-Editor 实现了在可动画 4D 高斯编辑中的逼真效果和一致性。我们对多个主体进行了全面实验，以验证所提技术的有效性，结果表明我们的方法在现有技术中具有明显优势。