# GSEdit: Efficient Text-Guided Editing of 3D Objects via Gaussian Splatting

arXiv: https://arxiv.org/abs/2403.05154 · HTML: https://mlsanity.com/paper/2403.05154/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 41 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 30 of 35)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (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)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021)
- [DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation](https://mlsanity.com/paper/2309.16653/) (2023)
- [pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2012.00926/) (2020)
- [GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2007.02442/) (2020)
- [KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs](https://mlsanity.com/paper/2103.13744/) (2021)
- [One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization](https://mlsanity.com/paper/2306.16928/) (2023)
- [FastNeRF: High-Fidelity Neural Rendering at 200FPS](https://mlsanity.com/paper/2103.10380/) (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)
- [StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis](https://mlsanity.com/paper/2110.08985/) (2021)
- [Baking Neural Radiance Fields for Real-Time View Synthesis](https://mlsanity.com/paper/2103.14645/) (2021)
- [Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors](https://mlsanity.com/paper/2306.17843/) (2023)
- [CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields](https://mlsanity.com/paper/2112.05139/) (2021)
- [GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting](https://mlsanity.com/paper/2311.14521/) (2023)
- [RealFusion: 360deg Reconstruction of Any Object from a Single Image](https://mlsanity.com/paper/2302.10663/) (2023)
- [Editing Conditional Radiance Fields](https://mlsanity.com/paper/2105.06466/) (2021)
- [AutoInt: Automatic Integration for Fast Neural Volume Rendering](https://mlsanity.com/paper/2012.01714/) (2020)
- [GaussianEditor: Editing 3D Gaussians Delicately with Text Instructions](https://mlsanity.com/paper/2311.16037/) (2023)
- [Fourier PlenOctrees for Dynamic Radiance Field Rendering in Real-time](https://mlsanity.com/paper/2202.08614/) (2022)

## Built on by (most cited first, 30 of 37)

- [Recent Advances in 3D Gaussian Splatting](https://mlsanity.com/paper/2403.11134/) (2024)
- [3D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunities](https://mlsanity.com/paper/2407.17418/) (2024)
- [EmbodiedOcc: Embodied 3D Occupancy Prediction for Vision-based Online Scene Understanding](https://mlsanity.com/paper/2412.04380/) (2024)
- [SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting Synthesis](https://mlsanity.com/paper/2411.16443/) (2024)
- [NANO3D: A Training-Free Approach for Efficient 3D Editing Without Masks](https://mlsanity.com/paper/2510.15019/) (2025)
- [GStex: Per-Primitive Texturing of 2D Gaussian Splatting for Decoupled Appearance and Geometry Modeling](https://mlsanity.com/paper/2409.12954/) (2024)
- [A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation](https://mlsanity.com/paper/2508.09977/) (2025)
- [DreamCatalyst: Fast and High-Quality 3D Editing via Controlling Editability and Identity Preservation](https://mlsanity.com/paper/2407.11394/) (2024)
- [Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation](https://mlsanity.com/paper/2503.01370/) (2025)
- [iSegMan: Interactive Segment-and-Manipulate 3D Gaussians](https://mlsanity.com/paper/2505.11934/) (2025)
- [UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping](https://mlsanity.com/paper/2502.01846/) (2025)
- [Implicit-ARAP: Efficient Handle-Guided Neural Field Deformation via Local Patch Meshing](https://mlsanity.com/paper/2405.12895/) (2024)
- [FruitNinja: 3D Object Interior Texture Generation with Gaussian Splatting](https://mlsanity.com/paper/2411.12089/) (2024)
- [ARAP-GS: Drag-driven As-Rigid-As-Possible 3D Gaussian Splatting Editing with Diffusion Prior](https://mlsanity.com/paper/2504.12788/) (2025)
- [Robust 3D-Masked Part-level Editing in 3D Gaussian Splatting with Regularized Score Distillation Sampling](https://mlsanity.com/paper/2507.11061/) (2025)
- [ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient Reconstruction](https://mlsanity.com/paper/2509.03775/) (2025)
- [VULCAN: Tool-Augmented Multi Agents for Iterative 3D Object Arrangement](https://mlsanity.com/paper/2512.22351/) (2025)
- [A3D: Does Diffusion Dream about 3D Alignment?](https://mlsanity.com/paper/2406.15020/) (2024)
- [CAGE-GS: High-fidelity Cage Based 3D Gaussian Splatting Deformation](https://mlsanity.com/paper/2504.12800/) (2025)
- [3D Scene Change Modeling With Consistent Multi-View Aggregation](https://mlsanity.com/paper/2512.22830/) (2025)
- [Vinedresser3D: Agentic Text-guided 3D Editing](https://mlsanity.com/paper/2602.19542/) (2026)
- [Efficient Semantic Splatting for Remote Sensing Multi-view Segmentation](https://mlsanity.com/paper/2412.05969/) (2024)
- [ProGDF: Progressive Gaussian Differential Field for Controllable and Flexible 3D Editing](https://mlsanity.com/paper/2412.08152/) (2024)
- [Rigidity-Aware 3D Gaussian Deformation from a Single Image](https://mlsanity.com/paper/2509.22222/) (2025)
- [Learning Unified Representation of 3D Gaussian Splatting](https://mlsanity.com/paper/2509.22917/) (2025)
- [ArtisanGS: Interactive Tools for Gaussian Splat Selection with AI and Human in the Loop](https://mlsanity.com/paper/2602.10173/) (2026)
- [Towards Generalized and Training-Free Text-Guided Semantic Manipulation](https://mlsanity.com/paper/2504.17269/) (2025)
- [Efficient multi-view training for 3D Gaussian Splatting](https://mlsanity.com/paper/2506.12727/) (2025)
- [Optimization-Free Style Transfer for 3D Gaussian Splats](https://mlsanity.com/paper/2508.05813/) (2025)
- [Cross-Temporal 3D Gaussian Splatting for Sparse-View Guided Scene Update](https://mlsanity.com/paper/2512.00534/) (2025)

## Abstract (arXiv)

We present GSEdit, a pipeline for text-guided 3D object editing based on Gaussian Splatting models. Our method enables the editing of the style and appearance of 3D objects without altering their main details, all in a matter of minutes on consumer hardware. We tackle the problem by leveraging Gaussian splatting to represent 3D scenes, and we optimize the model while progressively varying the image supervision by means of a pretrained image-based diffusion model. The input object may be given as a 3D triangular mesh, or directly provided as Gaussians from a generative model such as DreamGaussian. GSEdit ensures consistency across different viewpoints, maintaining the integrity of the original object's information. Compared to previously proposed methods relying on NeRF-like MLP models, GSEdit stands out for its efficiency, making 3D editing tasks much faster. Our editing process is refined via the application of the SDS loss, ensuring that our edits are both precise and accurate. Our comprehensive evaluation demonstrates that GSEdit effectively alters object shape and appearance following the given textual instructions while preserving their coherence and detail.