# MiraGe: Editable 2D Images using Gaussian Splatting

arXiv: https://arxiv.org/abs/2410.01521 · HTML: https://mlsanity.com/paper/2410.01521/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 38 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Builds on (most cited first, 23 of 23)

- [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)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [2D Gaussian Splatting for Geometrically Accurate Radiance Fields](https://mlsanity.com/paper/2403.17888/) (2024)
- [SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering](https://mlsanity.com/paper/2311.12775/) (2023)
- [Neural Fields in Visual Computing and Beyond](https://mlsanity.com/paper/2111.11426/) (2021)
- [PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics](https://mlsanity.com/paper/2311.12198/) (2023)
- [DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing](https://mlsanity.com/paper/2306.14435/) (2023)
- [WIRE: Wavelet Implicit Neural Representations](https://mlsanity.com/paper/2301.05187/) (2023)
- [Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold](https://mlsanity.com/paper/2305.10973/) (2023)
- [VR-GS: A Physical Dynamics-Aware Interactive Gaussian Splatting System in Virtual Reality](https://mlsanity.com/paper/2401.16663/) (2024)
- [PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation](https://mlsanity.com/paper/2409.18964/) (2024)
- [GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting](https://mlsanity.com/paper/2403.08551/) (2024)
- [FINER: Flexible spectral-bias tuning in Implicit NEural Representation by Variable-periodic Activation Functions](https://mlsanity.com/paper/2312.02434/) (2023)
- [GaMeS: Mesh-Based Adapting and Modification of Gaussian Splatting](https://mlsanity.com/paper/2402.01459/) (2024)
- [Hypernetwork functional image representation](https://mlsanity.com/paper/1902.10404/) (2019)
- [NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions](https://mlsanity.com/paper/2309.15426/) (2023)
- [GASP: Gaussian Splatting for Physic-Based Simulations](https://mlsanity.com/paper/2409.05819/) (2024)
- [D-MiSo: Editing Dynamic 3D Scenes using Multi-Gaussians Soup](https://mlsanity.com/paper/2405.14276/) (2024)
- [2D, 2.5D, or 3D? An Exploratory Study on Multilayer Network Visualisations in Virtual Reality](https://mlsanity.com/paper/2307.10674/) (2023)
- [An Inpainting-Infused Pipeline for Attire and Background Replacement](https://mlsanity.com/paper/2402.03501/) (2024)

## Built on by (most cited first, 12 of 12)

- [Instant GaussianImage: A Generalizable and Self-Adaptive Image Representation via 2D Gaussian Splatting](https://mlsanity.com/paper/2506.23479/) (2025)
- [VeGaS: Video Gaussian Splatting](https://mlsanity.com/paper/2411.11024/) (2024)
- [CLIPGaussian: Universal and Multimodal Style Transfer Based on Gaussian Splatting](https://mlsanity.com/paper/2505.22854/) (2025)
- [GaussianImage++: Boosted Image Representation and Compression with 2D Gaussian Splatting](https://mlsanity.com/paper/2512.19108/) (2025)
- [SGI: Structured 2D Gaussians for Efficient and Compact Large Image Representation](https://mlsanity.com/paper/2603.07789/) (2026)
- [Fast 2DGS: Efficient Image Representation with Deep Gaussian Prior](https://mlsanity.com/paper/2512.12774/) (2025)
- [REdiSplats: Ray Tracing for Editable Gaussian Splatting](https://mlsanity.com/paper/2503.12284/) (2025)
- [GaINeR: Geometry-Aware Implicit Network Representation](https://mlsanity.com/paper/2511.20924/) (2025)
- [ProxyImg: Towards Highly-Controllable Image Representation via Hierarchical Disentangled Proxy Embedding](https://mlsanity.com/paper/2602.01881/) (2026)
- [OmniStyle-INR: Universal and Multimodal Style Transfer for INRs](https://mlsanity.com/paper/2607.16362/) (2026)
- [Locality-Aware Density Control for Efficient Gaussian-based Image Representation](https://mlsanity.com/paper/2607.17896/) (2026)
- [TOM-GS: Editable Video Representation via Temporal Opacity Modulation of Static 3D Gaussians](https://mlsanity.com/paper/2607.22717/) (2026)

## Abstract (arXiv)

Implicit Neural Representations (INRs) approximate discrete data through continuous functions and are commonly used for encoding 2D images. Traditional image-based INRs employ neural networks to map pixel coordinates to RGB values, capturing shapes, colors, and textures within the network's weights. Recently, GaussianImage has been proposed as an alternative, using Gaussian functions instead of neural networks to achieve comparable quality and compression. Such a solution obtains a quality and compression ratio similar to classical INR models but does not allow image modification. In contrast, our work introduces a novel method, MiraGe, which uses mirror reflections to perceive 2D images in 3D space and employs flat-controlled Gaussians for precise 2D image editing. Our approach improves the rendering quality and allows realistic image modifications, including human-inspired perception of photos in the 3D world. Thanks to modeling images in 3D space, we obtain the illusion of 3D-based modification in 2D images. We also show that our Gaussian representation can be easily combined with a physics engine to produce physics-based modification of 2D images. Consequently, MiraGe allows for better quality than the standard approach and natural modification of 2D images