  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.
