GaussianAvatar-Editor：照片级真实感可驱动高斯头部头像编辑器
GaussianAvatar-Editor: Photorealistic Animatable Gaussian Head Avatar Editor
AI总结：
针对可驱动4D高斯头像编辑的运动遮挡与时空不一致问题，提出GaussianAvatar-Editor框架，结合加权Alpha混合方程与条件对抗学习，实现照片级真实感的一致编辑效果，性能优于现有方法。
摘要：
我们推出GaussianAvatar-Editor，这是一款用于文本驱动编辑可驱动高斯头部头像的创新框架，可在表情、姿态和视角上实现完全控制。与静态3D高斯编辑不同，编辑可驱动4D高斯头像存在运动遮挡和时空不一致性相关的挑战。为解决这些问题，我们提出了加权Alpha混合方程（Weighted Alpha Blending Equation, WABE）。该函数提升了可见高斯的混合权重，同时抑制对不可见高斯的影响，有效处理了编辑过程中的运动遮挡问题。此外，为提升编辑质量并确保4D一致性，我们将条件对抗学习融入编辑流程。该策略有助于优化编辑结果，并在整个动画过程中维持一致性。通过整合这些方法，我们的GaussianAvatar-Editor在可驱动4D高斯编辑中实现了照片级真实感且一致的结果。我们在多个对象上开展了全面实验，以验证所提技术的有效性，结果表明我们的方法优于现有方法。更多结果和代码可访问：[项目链接](https://xiangyueliu.github.io/GaussianAvatar-Editor/)。
英文摘要：
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/).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
