Recently, there has been a significant advancement in text-to-image diffusion models, leading to groundbreaking performance in 2D image generation. These advancements have been extended to 3D models, enabling the generation of novel 3D objects from textual descriptions. This has evolved into NeRF editing methods, which allow the manipulation of existing 3D objects through textual conditioning. However, existing NeRF editing techniques have faced limitations in their performance due to slow training speeds and the use of loss functions that do not adequately consider editing. To address this, here we present a novel 3D NeRF editing approach dubbed ED-NeRF by successfully embedding real-world scenes into the latent space of the latent diffusion model (LDM) through a unique refinement layer. This approach enables us to obtain a NeRF backbone that is not only faster but also more amenable to editing compared to traditional image space NeRF editing. Furthermore, we propose an improved loss function tailored for editing by migrating the delta denoising score (DDS) distillation loss, originally used in 2D image editing to the three-dimensional domain. This novel loss function surpasses the well-known score distillation sampling (SDS) loss in terms of suitability for editing purposes. Our experimental results demonstrate that ED-NeRF achieves faster editing speed while producing improved output quality compared to state-of-the-art 3D editing models.

翻译：近期，文本到图像扩散模型取得显著进展，在二维图像生成领域展现出突破性性能。这些进展已被拓展至三维模型领域，使得从文本描述生成新型三维对象成为可能，并进一步演化为NeRF编辑方法，允许通过文本条件操控现有三维对象。然而，现有NeRF编辑技术因训练速度缓慢及未充分考虑编辑任务的损失函数而面临性能局限。针对该问题，本文提出一种名为ED-NeRF的新型三维NeRF编辑方法，通过独特的精化层将真实场景成功嵌入潜扩散模型（LDM）的潜空间。与传统图像空间NeRF编辑相比，该方法不仅获得更快的NeRF主干网络，且更易于编辑操作。此外，我们通过将原用于二维图像编辑的Delta去噪分数（DDS）蒸馏损失迁移至三维领域，提出了一种专为编辑任务优化的改进损失函数。该新型损失函数在编辑适用性上显著优于广为人知的分数蒸馏采样（SDS）损失。实验结果表明，与最先进的三维编辑模型相比，ED-NeRF在保持更优输出质量的同时实现了更快的编辑速度。