# RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models

Source: [RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models](<https://www.sota2.com/research/paper/68fa71661e209131cea51303>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:06:39.354Z

Authors: Xingchen Zhou, Ying He, F. Richard Yu, Jianqiang Li, You Li

Published: 2023-06-09T04:49:31.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Image-driven 3D Scene Editing on Image-driven editing set 5 scenes (test)](<https://www.sota2.com/research/sota/image-driven-3d-scene-editing-on-image-driven-editing-set-5-scenes-test.md>): Image-driven 3D Scene Editing; Image-driven editing set 5 scenes (test); CLIP Direction Similarity: 16.46.

- [Text-driven 3D scene editing on Text-driven editing evaluation set 10 edits across 2 scenes](<https://www.sota2.com/research/sota/text-driven-3d-scene-editing-on-text-driven-editing-evaluation-set-10-edits-across-2-scenes.md>): Text-driven 3D scene editing; Text-driven editing evaluation set 10 edits across 2 scenes; CLIP Directionality Score: 16.34.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2306.05668>)

[PDF](<https://arxiv.org/pdf/2306.05668.pdf>)