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

arXiv: https://arxiv.org/abs/2310.02712 · HTML: https://mlsanity.com/paper/2310.02712/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 31 of its 34 references that are in the map.
- First posted on arXiv 4 Oct 2023.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 30 of 31)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [Zero-1-to-3: Zero-shot One Image to 3D Object](https://mlsanity.com/paper/2303.11328/) (2023)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023)
- [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021)
- [IBRNet: Learning Multi-View Image-Based Rendering](https://mlsanity.com/paper/2102.13090/) (2021)
- [KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs](https://mlsanity.com/paper/2103.13744/) (2021)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [Zero-Shot Text-Guided Object Generation with Dream Fields](https://mlsanity.com/paper/2112.01455/) (2021)
- [Image Segmentation Using Text and Image Prompts](https://mlsanity.com/paper/2112.10003/) (2021)
- [Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures](https://mlsanity.com/paper/2211.07600/) (2022)
- [Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions](https://mlsanity.com/paper/2303.12789/) (2023)
- [CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields](https://mlsanity.com/paper/2112.05139/) (2021)
- [Editing Conditional Radiance Fields](https://mlsanity.com/paper/2105.06466/) (2021)
- [NeRF-Editing: Geometry Editing of Neural Radiance Fields](https://mlsanity.com/paper/2205.04978/) (2022)
- [Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models](https://mlsanity.com/paper/2212.14704/) (2022)
- [ARF: Artistic Radiance Fields](https://mlsanity.com/paper/2206.06360/) (2022)
- [NeRF-Art: Text-Driven Neural Radiance Fields Stylization](https://mlsanity.com/paper/2212.08070/) (2022)
- [Delta Denoising Score](https://mlsanity.com/paper/2304.07090/) (2023)
- [Vox-E: Text-guided Voxel Editing of 3D Objects](https://mlsanity.com/paper/2303.12048/) (2023)
- [SINE: Semantic-driven Image-based NeRF Editing with Prior-guided Editing Field](https://mlsanity.com/paper/2303.13277/) (2023)
- [ReLU Fields: The Little Non-linearity That Could](https://mlsanity.com/paper/2205.10824/) (2022)
- [StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields](https://mlsanity.com/paper/2303.10598/) (2023)
- [PaletteNeRF: Palette-based Appearance Editing of Neural Radiance Fields](https://mlsanity.com/paper/2212.10699/) (2022)

## Built on by (most cited first, 20 of 20)

- [Contrastive Denoising Score for Text-guided Latent Diffusion Image Editing](https://mlsanity.com/paper/2311.18608/) (2023)
- [EditSplat: Multi-View Fusion and Attention-Guided Optimization for View-Consistent 3D Scene Editing with 3D Gaussian Splatting](https://mlsanity.com/paper/2412.11520/) (2024)
- [TweedieMix: Improving Multi-Concept Fusion for Diffusion-based Image/Video Generation](https://mlsanity.com/paper/2410.05591/) (2024)
- [Geometry in Style: 3D Stylization via Surface Normal Deformation](https://mlsanity.com/paper/2503.23241/) (2025)
- [Neural Radiance Field-based Visual Rendering: A Comprehensive Review](https://mlsanity.com/paper/2404.00714/) (2024)
- [Diffusion-Based Attention Warping for Consistent 3D Scene Editing](https://mlsanity.com/paper/2412.07984/) (2024)
- [SplatPainter: Interactive Authoring of 3D Gaussians from 2D Edits via Test-Time Training](https://mlsanity.com/paper/2512.05354/) (2025)
- [FreeInsert: Disentangled Text-Guided Object Insertion in 3D Gaussian Scene without Spatial Priors](https://mlsanity.com/paper/2505.01322/) (2025)
- [Apply Hierarchical-Chain-of-Generation to Complex Attributes Text-to-3D Generation](https://mlsanity.com/paper/2505.05505/) (2025)
- [Unified Editing of Panorama, 3D Scenes, and Videos Through Disentangled Self-Attention Injection](https://mlsanity.com/paper/2405.16823/) (2024)
- [Edit in 2D, Verify in 3D: Reinforcement Learning for Multi-view Consistent Scene Editing](https://mlsanity.com/paper/2603.03143/) (2026)
- [A3D: Does Diffusion Dream about 3D Alignment?](https://mlsanity.com/paper/2406.15020/) (2024)
- [Stable Score Distillation](https://mlsanity.com/paper/2507.09168/) (2025)
- [DisCo3D: Distilling Multi-View Consistency for 3D Scene Editing](https://mlsanity.com/paper/2508.01684/) (2025)
- [Splatent: Splatting Diffusion Latents for Novel View Synthesis](https://mlsanity.com/paper/2512.09923/) (2025)
- [Bringing NeRFs to the Latent Space: Inverse Graphics Autoencoder](https://mlsanity.com/paper/2410.22936/) (2024)
- [Focus on Neighbors and Know the Whole: Towards Consistent Dense Multiview Text-to-Image Generator for 3D Creation](https://mlsanity.com/paper/2408.13149/) (2024)
- [Fused-Planes: Why Train a Thousand Tri-Planes When You Can Share?](https://mlsanity.com/paper/2410.23742/) (2024)
- [InStyle: Instant Appearance Stylization of 3D Shapes](https://mlsanity.com/paper/2512.03683/) (2025)
- [CompoSE: Compositional Synthesis and Editing of 3D Shapes via Part-Aware Control](https://mlsanity.com/paper/2605.19350/) (2026)

## Abstract (arXiv)

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.