Recent advancements in diffusion models have shown remarkable proficiency in editing 2D images based on text prompts. However, extending these techniques to edit scenes in Neural Radiance Fields (NeRF) is complex, as editing individual 2D frames can result in inconsistencies across multiple views. Our crucial insight is that a NeRF scene's geometry can serve as a bridge to integrate these 2D edits. Utilizing this geometry, we employ a depth-conditioned ControlNet to enhance the coherence of each 2D image modification. Moreover, we introduce an inpainting approach that leverages the depth information of NeRF scenes to distribute 2D edits across different images, ensuring robustness against errors and resampling challenges. Our results reveal that this methodology achieves more consistent, lifelike, and detailed edits than existing leading methods for text-driven NeRF scene editing.

利用拓展到神经辐射场（NeRF）的编辑技术来编辑场景是复杂的，本文提出了利用NeRF场景的几何信息作为桥梁来整合2D编辑的方法，并引入了一种填充方法来确保对不同图像的2D编辑具有鲁棒性。结果表明，该方法比现有的文本驱动NeRF场景编辑方法实现了更加一致、逼真和详细的编辑效果。

DATENeRF: 基于深度的文本编辑技术