RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models
This work addresses the challenge of repainting content in NeRF for applications in 3D scene editing, though it is incremental as it builds on existing diffusion models.
The paper tackles the problem of editing 3D content in Neural Radiance Fields (NeRF) by proposing a framework that uses semantic masks and diffusion models to alter objects based on text prompts, enabling changes in appearance and shape across real-world and synthetic datasets.
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.