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了显著进展，推动了2D图像生成的突破性性能。这些进展已拓展至3D模型，使得能够从文本描述中生创新的3D对象，并进一步演化为NeRF编辑方法，允许通过文本条件操控现有3D对象。然而，现有NeRF编辑技术因训练速度慢且使用的损失函数未充分考虑编辑需求而面临性能局限。为此，本文提出一种新颖的3D NeRF编辑方法——ED-NeRF，通过独特的精化层将真实世界场景成功嵌入潜扩散模型（LDM）的潜空间中。相较于传统的图像空间NeRF编辑，该方法不仅使NeRF主干网络更快，而且更易于编辑。此外，我们通过将原本用于2D图像编辑的增量去噪分数（DDS）蒸馏损失迁移至三维领域，提出了一种专为编辑优化的改进损失函数。这一新型损失函数在编辑适用性方面超越了著名的分数蒸馏采样（SDS）损失。实验结果表明，与最先进的3D编辑模型相比，ED-NeRF在实现更快编辑速度的同时，显著提升了输出质量。