The emergence of Neural Radiance Fields (NeRF) has promoted the development of synthesized high-fidelity views of the intricate real world. However, it is still a very demanding task to repaint the content in NeRF. In this paper, we propose a novel framework that can take RGB images as input and alter the 3D content in neural scenes. Our work leverages existing diffusion models to guide changes in the designated 3D content. Specifically, we semantically select the target object and a pre-trained diffusion model will guide the NeRF model to generate new 3D objects, which can improve the editability, diversity, and application range of NeRF. Experiment results show that our algorithm is effective for editing 3D objects in NeRF under different text prompts, including editing appearance, shape, and more. We validate our method on both real-world datasets and synthetic-world datasets for these editing tasks. Please visit https://repaintnerf.github.io for a better view of our results.

本文提出了一种新颖的框架，可以利用扩散模型实现对神经场景中的3D内容进行编辑，此方法可以有效地改善NeRF的可编辑性、多样性和应用范围，验证结果表明在不同的文本提示下，该算法对NeRF中的3D目标具有改进的效果。

基于语义掩模和扩散模型的 NeRF 编辑