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.
三维编辑在许多领域（如游戏和虚拟现实）中扮演着关键角色。传统的三维编辑方法，依赖于像网格和点云这样的表示，往往在真实地描绘复杂场景方面存在不足。另一方面，基于隐式三维表示的方法，如神经辐射场（NeRF），虽然能有效渲染复杂场景，但却因处理速度慢和对特定场景区域控制有限而受到限制。针对这些挑战，我们的论文介绍了GaussianEditor，一种基于高斯飞溅（GS）的创新高效三维编辑算法，GS是一种新颖的三维表示。GaussianEditor通过我们提出的高斯语义跟踪来提高编辑中的精确度和控制，该技术在整个训练过程中跟踪编辑目标。此外，我们提出了分层高斯飞溅（HGS），在二维扩散模型的随机生成指导下实现稳定和精细的结果。我们还开发了有效的对象移除和整合的编辑策略，这是现有方法的一个挑战性任务。我们的综合实验展示了GaussianEditor在控制、效率和快速性能方面的优越性，标志着三维编辑领域的重大进展。