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://starstesla.github.io/repaintnerf for a better view of our results.

翻译：神经辐射场（NeRF）的出现推动了真实复杂世界高保真视图合成技术的发展。然而，对NeRF中的内容进行重绘仍是一项极具挑战的任务。本文提出了一种新型框架，能够以RGB图像为输入，对神经场景中的三维内容进行修改。我们的方法利用现有扩散模型引导指定三维内容的变更：具体而言，通过语义方式选择目标对象，预训练的扩散模型会引导NeRF模型生成新的三维物体，从而提升NeRF的可编辑性、多样性及适用范围。实验结果表明，该算法在多种文本提示下（包括外观编辑、形状编辑等）均能有效实现对NeRF中三维物体的编辑。针对这些编辑任务，我们在真实世界数据集与合成世界数据集上均验证了方法的有效性。更多结果展示请访问https://starstesla.github.io/repaintnerf。