Abstract
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 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 3DGS, 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.
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Figure 1
Results of GaussianEditor.
GaussianEditor offers swift, controllable, and versatile 3D editing. A single editing ses