Edit-DiffNeRF: Editing 3D Neural Radiance Fields using 2D Diffusion Model

• URL: http://arxiv.org/abs/2306.09551v1
• Date: Thu, 15 Jun 2023 23:41:58 GMT
• Title: Edit-DiffNeRF: Editing 3D Neural Radiance Fields using 2D Diffusion Model
• Authors: Lu Yu, Wei Xiang, Kang Han
• Abstract summary: Combination of pretrained diffusion models with neural radiance fields (NeRFs) has emerged as a promising approach for text-to-3D generation. We propose the Edit-DiffNeRF framework, which is composed of a frozen diffusion model, a proposed delta module to edit the latent semantic space of the diffusion model, and a NeRF. Our proposed method has been shown to effectively edit real-world 3D scenes, resulting in 25% improvement in the alignment of the performed 3D edits with text instructions.
• Score: 11.05302598034426
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent research has demonstrated that the combination of pretrained diffusion models with neural radiance fields (NeRFs) has emerged as a promising approach for text-to-3D generation. Simply coupling NeRF with diffusion models will result in cross-view inconsistency and degradation of stylized view syntheses. To address this challenge, we propose the Edit-DiffNeRF framework, which is composed of a frozen diffusion model, a proposed delta module to edit the latent semantic space of the diffusion model, and a NeRF. Instead of training the entire diffusion for each scene, our method focuses on editing the latent semantic space in frozen pretrained diffusion models by the delta module. This fundamental change to the standard diffusion framework enables us to make fine-grained modifications to the rendered views and effectively consolidate these instructions in a 3D scene via NeRF training. As a result, we are able to produce an edited 3D scene that faithfully aligns to input text instructions. Furthermore, to ensure semantic consistency across different viewpoints, we propose a novel multi-view semantic consistency loss that extracts a latent semantic embedding from the input view as a prior, and aim to reconstruct it in different views. Our proposed method has been shown to effectively edit real-world 3D scenes, resulting in 25% improvement in the alignment of the performed 3D edits with text instructions compared to prior work.

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