This paper enables high-fidelity, transferable NeRF editing by frequency
decomposition. Recent NeRF editing pipelines lift 2D stylization results to 3D
scenes while suffering from blurry results, and fail to capture detailed
structures caused by the inconsistency between 2D editings. Our critical
insight is that low-frequency components of images are more
multiview-consistent after editing compared with their high-frequency parts.
Moreover, the appearance style is mainly exhibited on the low-frequency
components, and the content details especially reside in high-frequency parts.
This motivates us to perform editing on low-frequency components, which results
in high-fidelity edited scenes. In addition, the editing is performed in the
low-frequency feature space, enabling stable intensity control and novel scene
transfer. Comprehensive experiments conducted on photorealistic datasets
demonstrate the superior performance of high-fidelity and transferable NeRF
editing. The project page is at https://aigc3d.github.io/freditor.

通过频率分解实现高保真、可迁移的 NeRF 编辑。

Freditor：基于频率分解的高保真可迁移 NeRF 编辑

Freditor: High-Fidelity and Transferable NeRF Editing by Frequency  Decomposition

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.

我们介绍了一种名为 ED-NeRF 的创新的三维 NeRF 编辑方法，通过将真实场景嵌入潜变扩散模型的潜入空间中，通过独特的细化层，实现了更快速和更易编辑的 NeRF 骨干，同时我们提出了一种改进的针对编辑的损失函数，这一损失函数在编辑目的上优于传统的得分扩散采样损失。实验结果表明，ED-NeRF 在更快的编辑速度和更好的输出质量方面胜过现有的最先进的三维编辑模型。

ED-NeRF：使用潜空间进行高效的文本导向 3D 场景编辑

ED-NeRF: Efficient Text-Guided Editing of 3D Scene using Latent Space  NeRF

With the popularity of implicit neural representations, or neural radiance
fields (NeRF), there is a pressing need for editing methods to interact with
the implicit 3D models for tasks like post-processing reconstructed scenes and
3D content creation. While previous works have explored NeRF editing from
various perspectives, they are restricted in editing flexibility, quality, and
speed, failing to offer direct editing response and instant preview. The key
challenge is to conceive a locally editable neural representation that can
directly reflect the editing instructions and update instantly. To bridge the
gap, we propose a new interactive editing method and system for implicit
representations, called Seal-3D, which allows users to edit NeRF models in a
pixel-level and free manner with a wide range of NeRF-like backbone and preview
the editing effects instantly. To achieve the effects, the challenges are
addressed by our proposed proxy function mapping the editing instructions to
the original space of NeRF models and a teacher-student training strategy with
local pretraining and global finetuning. A NeRF editing system is built to
showcase various editing types. Our system can achieve compelling editing
effects with an interactive speed of about 1 second.

通过提出一种新的交互式编辑方法和系统，名为 Seal-3D，可以实现对隐式表示的编辑，允许用户以像素级和自由的方式编辑 NeRF 模型，并即时预览编辑效果。