# GeoDiffuser: Geometry-Based Image Editing with Diffusion Models

arXiv: https://arxiv.org/abs/2404.14403 · HTML: https://mlsanity.com/paper/2404.14403/

## Key facts

- Cited by 34 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 53 of its 72 references that are in the map.
- First posted on arXiv 22 Apr 2024.

## 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 53)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (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)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Zero-1-to-3: Zero-shot One Image to 3D Object](https://mlsanity.com/paper/2303.11328/) (2023)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields](https://mlsanity.com/paper/2011.12100/) (2020)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023)
- [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://mlsanity.com/paper/2302.12288/) (2023)
- [Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions](https://mlsanity.com/paper/2303.12789/) (2023)
- [Emergent Correspondence from Image Diffusion](https://mlsanity.com/paper/2306.03881/) (2023)
- [CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields](https://mlsanity.com/paper/2112.05139/) (2021)
- [Understanding Diffusion Models: A Unified Perspective](https://mlsanity.com/paper/2208.11970/) (2022)

## Built on by (most cited first, 30 of 34)

- [Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video Generation Control](https://mlsanity.com/paper/2501.03847/) (2025)
- [SceneFactor: Factored Latent 3D Diffusion for Controllable 3D Scene Generation](https://mlsanity.com/paper/2412.01801/) (2024)
- [FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video Generation](https://mlsanity.com/paper/2506.01144/) (2025)
- [Training-free Geometric Image Editing on Diffusion Models](https://mlsanity.com/paper/2507.23300/) (2025)
- [3D-Fixup: Advancing Photo Editing with 3D Priors](https://mlsanity.com/paper/2505.10566/) (2025)
- [Compass Control: Multi Object Orientation Control for Text-to-Image Generation](https://mlsanity.com/paper/2504.06752/) (2025)
- [ORIDa: Object-centric Real-world Image Composition Dataset](https://mlsanity.com/paper/2506.08964/) (2025)
- [SpaceControl: Introducing Test-Time Spatial Control to 3D Generative Modeling](https://mlsanity.com/paper/2512.05343/) (2025)
- [FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation](https://mlsanity.com/paper/2412.02690/) (2024)
- [BlenderFusion: 3D-Grounded Visual Editing and Generative Compositing](https://mlsanity.com/paper/2506.17450/) (2025)
- [InstantEdit: Text-Guided Few-Step Image Editing with Piecewise Rectified Flow](https://mlsanity.com/paper/2508.06033/) (2025)
- [Geometry-Aware Diffusion Models for Multiview Scene Inpainting](https://mlsanity.com/paper/2502.13335/) (2025)
- [GenHSI: Controllable Generation of Human-Scene Interaction Videos](https://mlsanity.com/paper/2506.19840/) (2025)
- [A Diffusion-Based Framework for Occluded Object Movement](https://mlsanity.com/paper/2504.01873/) (2025)
- [Coupled Diffusion Sampling for Training-Free Multi-View Image Editing](https://mlsanity.com/paper/2510.14981/) (2025)
- [SeeThrough3D: Occlusion Aware 3D Control in Text-to-Image Generation](https://mlsanity.com/paper/2602.23359/) (2026)
- [CameraMaster: Unified Camera Semantic-Parameter Control for Photography Retouching](https://mlsanity.com/paper/2511.21024/) (2025)
- [Ctrl&Shift: High-Quality Geometry-Aware Object Manipulation in Visual Generation](https://mlsanity.com/paper/2602.11440/) (2026)
- [Direct 3D-Aware Object Insertion via Decomposed Visual Proxies](https://mlsanity.com/paper/2606.06601/) (2026)
- [Geometric Image Editing via Effects-Sensitive In-Context Inpainting with Diffusion Transformers](https://mlsanity.com/paper/2602.08388/) (2026)
- [FlexAM: Flexible Appearance-Motion Decomposition for Versatile Video Generation Control](https://mlsanity.com/paper/2602.13185/) (2026)
- [PhyEdit: Towards Real-World Object Manipulation via Physically-Grounded Image Editing](https://mlsanity.com/paper/2604.07230/) (2026)
- [GeoEdit: Geometry-Aware Object Editing via Dual-Branch Denoising](https://mlsanity.com/paper/2606.30003/) (2026)
- [GenHeld: Generating and Editing Handheld Objects](https://mlsanity.com/paper/2406.05059/) (2024)
- [Unsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector Quantization](https://mlsanity.com/paper/2410.20573/) (2024)
- [Pattern Analogies: Learning to Perform Programmatic Image Edits by Analogy](https://mlsanity.com/paper/2412.12463/) (2024)
- [MotionDiff: Training-free Zero-shot Interactive Motion Editing via Flow-assisted Multi-view Diffusion](https://mlsanity.com/paper/2503.17695/) (2025)
- [MoCA3D: Monocular 3D Bounding Box Prediction in the Image Plane](https://mlsanity.com/paper/2603.19538/) (2026)
- [ObjectMorpher: 3D-Aware Image Editing via Deformable 3DGS Models](https://mlsanity.com/paper/2603.28152/) (2026)
- [Thinking in Boxes: 3D Editing in Real Images Made Easy](https://mlsanity.com/paper/2606.20556/) (2026)

## Abstract (arXiv)

The success of image generative models has enabled us to build methods that can edit images based on text or other user input. However, these methods are bespoke, imprecise, require additional information, or are limited to only 2D image edits. We present GeoDiffuser, a zero-shot optimization-based method that unifies common 2D and 3D image-based object editing capabilities into a single method. Our key insight is to view image editing operations as geometric transformations. We show that these transformations can be directly incorporated into the attention layers in diffusion models to implicitly perform editing operations. Our training-free optimization method uses an objective function that seeks to preserve object style but generate plausible images, for instance with accurate lighting and shadows. It also inpaints disoccluded parts of the image where the object was originally located. Given a natural image and user input, we segment the foreground object using SAM and estimate a corresponding transform which is used by our optimization approach for editing. GeoDiffuser can perform common 2D and 3D edits like object translation, 3D rotation, and removal. We present quantitative results, including a perceptual study, that shows how our approach is better than existing methods. Visit https://ivl.cs.brown.edu/research/geodiffuser.html for more information.