3D editing plays a crucial role in many areas such as gaming and virtual reality. Traditional 3D editing methods, which rely on representations like meshes and point clouds, often fall short in realistically depicting complex scenes. On the other hand, methods based on implicit 3D representations, like Neural Radiance Field (NeRF), render complex scenes effectively but suffer from slow processing speeds and limited control over specific scene areas. In response to these challenges, our paper presents GaussianEditor, an innovative and efficient 3D editing algorithm based on Gaussian Splatting (GS), a novel 3D representation. GaussianEditor enhances precision and control in editing through our proposed Gaussian semantic tracing, which traces the editing target throughout the training process. Additionally, we propose Hierarchical Gaussian splatting (HGS) to achieve stabilized and fine results under stochastic generative guidance from 2D diffusion models. We also develop editing strategies for efficient object removal and integration, a challenging task for existing methods. Our comprehensive experiments demonstrate GaussianEditor's superior control, efficacy, and rapid performance, marking a significant advancement in 3D editing. Project Page: https://buaacyw.github.io/gaussian-editor/

翻译：3D编辑在游戏和虚拟现实等多个领域扮演着关键角色。传统的3D编辑方法依赖网格和点云等表示形式，在真实感描绘复杂场景方面往往力有不逮。另一方面，基于隐式3D表示的方法，如神经辐射场（NeRF），虽能有效渲染复杂场景，但存在处理速度慢且对特定场景区域控制有限的问题。针对这些挑战，本文提出GaussianEditor——一种基于新型3D表示高斯泼溅（GS）的创新高效3D编辑算法。通过提出的高斯语义追踪技术，GaussianEditor在训练过程中全程追踪编辑目标，从而提升了编辑的精度与可控性。此外，我们提出分层高斯泼溅（HGS），在2D扩散模型的随机生成引导下实现稳定且精细的编辑结果。我们还开发了针对高效物体移除与整合的编辑策略，这是现有方法面临的挑战性任务。全面实验证明，GaussianEditor在控制力、效能和处理速度上均表现卓越，标志着3D编辑领域的重大进步。项目主页：https://buaacyw.github.io/gaussian-editor/