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：三维编辑在游戏和虚拟现实等多个领域扮演着关键角色。传统三维编辑方法依赖于网格和点云等表示方式，往往难以逼真地呈现复杂场景。另一方面，基于神经辐射场等隐式三维表示的方法虽能有效渲染复杂场景，但存在处理速度慢、对特定场景区域控制有限等问题。针对这些挑战，本文提出高斯编辑器，一种基于新型三维表示——高斯溅射的创新高效三维编辑算法。高斯编辑器通过我们提出的高斯语义追踪技术增强了编辑的精确性与可控性，该技术能在整个训练过程中精准追踪编辑目标。此外，我们提出分层高斯溅射方法，在二维扩散模型的随机生成引导下实现稳定精细的编辑结果。我们还开发了高效物体移除与集成的编辑策略，这是现有方法难以应对的挑战。全面的实验表明，高斯编辑器在控制力、效率和速度上具有显著优势，标志着三维编辑领域的重大进步。项目页面：https://buaacyw.github.io/gaussian-editor/