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.

翻译：神经辐射场（NeRF）的出现推动了复杂真实世界高保真合成视图的发展。然而，对NeRF中的内容进行重绘仍是一项极具挑战性的任务。本文提出一种新型框架，能够以RGB图像为输入，改变神经场景中的三维内容。我们的工作利用现有扩散模型来引导指定三维内容的修改。具体而言，我们通过语义方式选择目标对象，并由预训练扩散模型引导NeRF模型生成新的三维对象，从而提升NeRF的可编辑性、多样性及应用范围。实验结果表明，该算法在不同文本提示下（包括外观、形状等编辑任务）对NeRF中三维对象的编辑具有有效性。我们针对这些编辑任务在真实世界数据集与合成世界数据集上验证了该方法。更多结果请访问https://repaintnerf.github.io。