# Reflecting Reality: Enabling Diffusion Models to Produce Faithful Mirror Reflections

arXiv: https://arxiv.org/abs/2409.14677 · HTML: https://mlsanity.com/paper/2409.14677/

## Key facts

- Cited by 16 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 16 of its 65 references that are in the map.
- First posted on arXiv 23 Sep 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, 16 of 16)

- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion](https://mlsanity.com/paper/2403.06976/) (2024)
- [A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting](https://mlsanity.com/paper/2312.03594/) (2023)
- [ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion](https://mlsanity.com/paper/2403.18818/) (2024)
- [HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion Models](https://mlsanity.com/paper/2312.14091/) (2023)
- [Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for now](https://mlsanity.com/paper/2311.17138/) (2023)
- [MirrorGaussian: Reflecting 3D Gaussians for Reconstructing Mirror Reflections](https://mlsanity.com/paper/2405.11921/) (2024)
- [Mirror-3DGS: Incorporating Mirror Reflections into 3D Gaussian Splatting](https://mlsanity.com/paper/2404.01168/) (2024)
- [PromptRR: Diffusion Models as Prompt Generators for Single Image Reflection Removal](https://mlsanity.com/paper/2402.02374/) (2024)

## Built on by (most cited first, 16 of 16)

- [R3D2: Realistic 3D Asset Insertion via Diffusion for Autonomous Driving Simulation](https://mlsanity.com/paper/2506.07826/) (2025)
- [Compass Control: Multi Object Orientation Control for Text-to-Image Generation](https://mlsanity.com/paper/2504.06752/) (2025)
- [MirrorVerse: Pushing Diffusion Models to Realistically Reflect the World](https://mlsanity.com/paper/2504.15397/) (2025)
- [MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation](https://mlsanity.com/paper/2411.17945/) (2024)
- [SeeThrough3D: Occlusion Aware 3D Control in Text-to-Image Generation](https://mlsanity.com/paper/2602.23359/) (2026)
- [Reflect3r: Single-View 3D Stereo Reconstruction Aided by Mirror Reflections](https://mlsanity.com/paper/2509.20607/) (2025)
- [Refracting Reality: Generating Images with Realistic Transparent Objects](https://mlsanity.com/paper/2511.17340/) (2025)
- [Octree Diffusion for Semantic Scene Generation and Completion](https://mlsanity.com/paper/2509.16483/) (2025)
- [PRIMEdit: Probability Redistribution for Instance-Aware Multi-Object Video Editing With a New Benchmark Dataset](https://mlsanity.com/paper/2412.12877/) (2024)
- [Reflection Generation for Composite Image Using Diffusion Model](https://mlsanity.com/paper/2604.02168/) (2026)
- [PhysMirror: Physics-Aware Mirror Object Generation](https://mlsanity.com/paper/2607.03470/) (2026)
- [MirrorWorld: Taming Video Diffusion Models for Mirror Reflection Generation](https://mlsanity.com/paper/2608.07463/) (2026)
- [Fill My Mirror: Geometry-Constrained Mirror Inpainting](https://mlsanity.com/paper/2609.03740/) (2026)
- [Reflection-aware Generative Novel View Synthesis](https://mlsanity.com/paper/2609.05382/) (2026)
- [PhysReflect: Geometry and Perception Guided Diffusion for Physically-Plausible Mirror Reflections](https://mlsanity.com/paper/2609.23442/) (2026)
- [DiffusionShadow: Diffusion-based Shadow Caching for Neural Volume Rendering](https://mlsanity.com/paper/2609.30658/) (2026)

## Abstract (arXiv)

We tackle the problem of generating highly realistic and plausible mirror reflections using diffusion-based generative models. We formulate this problem as an image inpainting task, allowing for more user control over the placement of mirrors during the generation process. To enable this, we create SynMirror, a large-scale dataset of diverse synthetic scenes with objects placed in front of mirrors. SynMirror contains around 198k samples rendered from 66k unique 3D objects, along with their associated depth maps, normal maps and instance-wise segmentation masks, to capture relevant geometric properties of the scene. Using this dataset, we propose a novel depth-conditioned inpainting method called MirrorFusion, which generates high-quality, realistic, shape and appearance-aware reflections of real-world objects. MirrorFusion outperforms state-of-the-art methods on SynMirror, as demonstrated by extensive quantitative and qualitative analysis. To the best of our knowledge, we are the first to successfully tackle the challenging problem of generating controlled and faithful mirror reflections of an object in a scene using diffusion-based models. SynMirror and MirrorFusion open up new avenues for image editing and augmented reality applications for practitioners and researchers alike. The project page is available at: https://val.cds.iisc.ac.in/reflecting-reality.github.io/.