  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/
