GaussEdit: Adaptive 3D Scene Editing with Text and Image Prompts
Not on the map. Why?
Abstract (arXiv)
This paper presents GaussEdit, a framework for adaptive 3D scene editing guided by text and image prompts. GaussEdit leverages 3D Gaussian Splatting as its backbone for scene representation, enabling convenient Region of Interest selection and efficient editing through a three-stage process. The first stage involves initializing the 3D Gaussians to ensure high-quality edits. The second stage employs an Adaptive Global-Local Optimization strategy to balance global scene coherence and detailed local edits and a category-guided regularization technique to alleviate the Janus problem. The final stage enhances the texture of the edited objects using a sophisticated image-to-image synthesis technique, ensuring that the results are visually realistic and align closely with the given prompts. Our experimental results demonstrate that GaussEdit surpasses existing methods in editing accuracy, visual fidelity, and processing speed. By successfully embedding user-specified concepts into 3D scenes, GaussEdit is a powerful tool for detailed and user-driven 3D scene editing, offering significant improvements over traditional methods.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 57 of its 63 references that are in the map. First posted on arXiv 30 Sep 2025.
Builds on
57 of 63 references.
References · 57
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2020Denoising Diffusion Implicit Modelscited by 9,781
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
- 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
- 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
- 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
- 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
- 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,025
- 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,611
- 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,431
38 more
- 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,245
- 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,964
- 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,937
- 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,712
- 2020pixelNeRF: Neural Radiance Fields from One or Few Imagescited by 1,584
- 2022Magic3D: High-Resolution Text-to-3D Content Creationcited by 1,290
- 2023ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillationcited by 1,219
- 2022Imagic: Text-Based Real Image Editing with Diffusion Modelscited by 1,178
- 2022Multi-Concept Customization of Text-to-Image Diffusioncited by 1,106
- 2023MVDream: Multi-view Diffusion for 3D Generationcited by 938
- 2021Blended Diffusion for Text-driven Editing of Natural Imagescited by 927
- 2023Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creationcited by 687
- 2022DiffEdit: Diffusion-based semantic image editing with mask guidancecited by 647
- 2021Zero-Shot Text-Guided Object Generation with Dream Fieldscited by 513
- 2022Latent-NeRF for Shape-Guided Generation of 3D Shapes and Texturescited by 506
- 2019BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networkscited by 490
- 2023Instruct-NeRF2NeRF: Editing 3D Scenes with Instructionscited by 484
- 2022Decomposing NeRF for Editing via Feature Field Distillationcited by 371
- 2021Text2Mesh: Text-Driven Neural Stylization for Meshescited by 334
- 2023TEXTure: Text-Guided Texturing of 3D Shapescited by 304
- 2022CLIP-Mesh: Generating textured meshes from text using pretrained image-text modelscited by 281
- 2023GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Modelscited by 255
- 2023DreamBooth3D: Subject-Driven Text-to-3D Generationcited by 235
- 2021Editing Conditional Radiance Fieldscited by 222
- 2022NeRF-Editing: Geometry Editing of Neural Radiance Fieldscited by 217
- 2023DreamEditor: Text-Driven 3D Scene Editing with Neural Fieldscited by 155
- 2022NeuMesh: Learning Disentangled Neural Mesh-based Implicit Field for Geometry and Texture Editingcited by 142
- 2023Re-imagine the Negative Prompt Algorithm: Transform 2D Diffusion into 3D, alleviate Janus problem and Beyondcited by 139
- 2021MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesiscited by 133
- 2022NeRF-Art: Text-Driven Neural Radiance Fields Stylizationcited by 126
- 2023Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fieldscited by 123
- 2023Delta Denoising Scorecited by 121
- 2023Vox-E: Text-guided Voxel Editing of 3D Objectscited by 115
- 2021NeuTex: Neural Texture Mapping for Volumetric Neural Renderingcited by 98
- 2022Compressible-composable NeRF via Rank-residual Decompositioncited by 92
- 2022TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting Decompositioncited by 83
- 2022Deforming Radiance Fields with Cagescited by 78
- 2024TIP-Editor: An Accurate 3D Editor Following Both Text-Prompts And Image-Promptscited by 77
50 most cited of 57 All 57 (JSON)
Built on by
Citing papers per year: 2026: 1.
1 paper.