# IllumiNeRF: 3D Relighting Without Inverse Rendering

arXiv: https://arxiv.org/abs/2406.06527 · HTML: https://mlsanity.com/paper/2406.06527/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 62 references that are in the map.
- First posted on arXiv 10 Jun 2024.

## Builds on (most cited first, 30 of 41)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (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)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [Volume Rendering of Neural Implicit Surfaces](https://mlsanity.com/paper/2106.12052/) (2021)
- [LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation](https://mlsanity.com/paper/2402.05054/) (2024)
- [Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields](https://mlsanity.com/paper/2304.06706/) (2023)
- [UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction](https://mlsanity.com/paper/2104.10078/) (2021)
- [Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields](https://mlsanity.com/paper/2112.03907/) (2021)
- [NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis](https://mlsanity.com/paper/2012.03927/) (2020)
- [NeRD: Neural Reflectance Decomposition from Image Collections](https://mlsanity.com/paper/2012.03918/) (2020)
- [Kubric: A scalable dataset generator](https://mlsanity.com/paper/2203.03570/) (2022)
- [Extracting Triangular 3D Models, Materials, and Lighting From Images](https://mlsanity.com/paper/2111.12503/) (2021)
- [PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting](https://mlsanity.com/paper/2104.00674/) (2021)
- [Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image](https://mlsanity.com/paper/1905.02722/) (2019)
- [Neural Reflectance Fields for Appearance Acquisition](https://mlsanity.com/paper/2008.03824/) (2020)
- [Modeling Indirect Illumination for Inverse Rendering](https://mlsanity.com/paper/2204.06837/) (2022)
- [TensoIR: Tensorial Inverse Rendering](https://mlsanity.com/paper/2304.12461/) (2023)
- [Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising](https://mlsanity.com/paper/2206.03380/) (2022)
- [Single Image Portrait Relighting](https://mlsanity.com/paper/1905.00824/) (2019)
- [NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination](https://mlsanity.com/paper/2106.01970/) (2021)
- [DiLightNet: Fine-grained Lighting Control for Diffusion-based Image Generation](https://mlsanity.com/paper/2402.11929/) (2024)

## Built on by (most cited first, 29 of 29)

- [LumiNet: Latent Intrinsics Meets Diffusion Models for Indoor Scene Relighting](https://mlsanity.com/paper/2412.00177/) (2024)
- [CubeDiff: Repurposing Diffusion-Based Image Models for Panorama Generation](https://mlsanity.com/paper/2501.17162/) (2025)
- [SpectroMotion: Dynamic 3D Reconstruction of Specular Scenes](https://mlsanity.com/paper/2410.17249/) (2024)
- [ScribbleLight: Single Image Indoor Relighting with Scribbles](https://mlsanity.com/paper/2411.17696/) (2024)
- [ZeroComp: Zero-shot Object Compositing from Image Intrinsics via Diffusion](https://mlsanity.com/paper/2410.08168/) (2024)
- [LightSwitch: Multi-view Relighting with Material-guided Diffusion](https://mlsanity.com/paper/2508.06494/) (2025)
- [Generative Multiview Relighting for 3D Reconstruction under Extreme Illumination Variation](https://mlsanity.com/paper/2412.15211/) (2024)
- [GUS-IR: Gaussian Splatting with Unified Shading for Inverse Rendering](https://mlsanity.com/paper/2411.07478/) (2024)
- [VideoMat: Extracting PBR Materials from Video Diffusion Models](https://mlsanity.com/paper/2506.09665/) (2025)
- [LuxRemix: Lighting Decomposition and Remixing for Indoor Scenes](https://mlsanity.com/paper/2601.15283/) (2026)
- [ROGR: Relightable 3D Objects using Generative Relighting](https://mlsanity.com/paper/2510.03163/) (2025)
- [SimVS: Simulating World Inconsistencies for Robust View Synthesis](https://mlsanity.com/paper/2412.07696/) (2024)
- [NeAR: Coupled Neural Asset-Renderer Stack](https://mlsanity.com/paper/2511.18600/) (2025)
- [GR3EN: Generative Relighting for 3D Environments](https://mlsanity.com/paper/2601.16272/) (2026)
- [PI-Light: Physics-Inspired Diffusion for Full-Image Relighting](https://mlsanity.com/paper/2601.22135/) (2026)
- [MADrive: Memory-Augmented Driving Scene Modeling](https://mlsanity.com/paper/2506.21520/) (2025)
- [MVLight: Relightable Text-to-3D Generation via Light-conditioned Multi-View Diffusion](https://mlsanity.com/paper/2411.11475/) (2024)
- [MatSpray: Fusing 2D Material World Knowledge on 3D Geometry](https://mlsanity.com/paper/2512.18314/) (2025)
- [Learning Latent Proxies for Controllable Single-Image Relighting](https://mlsanity.com/paper/2603.15555/) (2026)
- [TranSplat: Instant Object Relighting in Gaussian Splatting via Spherical Harmonic Radiance Transfer](https://mlsanity.com/paper/2503.22676/) (2025)
- [SyncLight: Single-Edit Multi-View Relighting](https://mlsanity.com/paper/2601.16981/) (2026)
- [Joint Shadow Generation and Relighting via Light-Geometry Interaction Maps](https://mlsanity.com/paper/2602.21820/) (2026)
- [SeasonScapes: Learning Large-scale Re-lightable 3D Landscapes with Seasonal Variation from Sparse Webcams](https://mlsanity.com/paper/2605.09039/) (2026)
- [WildRelight: A Real-World Benchmark and Physics-Guided Adaptation for Single-Image Relighting](https://mlsanity.com/paper/2605.11696/) (2026)
- [BRDFusion: Physics Meets Generation for Urban Scene Inverse Rendering](https://mlsanity.com/paper/2606.17049/) (2026)
- [DANTE-W: Diffuse Albedo Neural Texturing in the Wild](https://mlsanity.com/paper/2606.30677/) (2026)
- [Diffusion-Based Material Regularization for Physics-Based Inverse Rendering](https://mlsanity.com/paper/2606.31065/) (2026)
- [LumiTokens: 3D Relighting via Token-Space Lighting Transformation](https://mlsanity.com/paper/2608.18215/) (2026)
- [RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting](https://mlsanity.com/paper/2609.07414/) (2026)

## Abstract (arXiv)

Existing methods for relightable view synthesis -- using a set of images of an object under unknown lighting to recover a 3D representation that can be rendered from novel viewpoints under a target illumination -- are based on inverse rendering, and attempt to disentangle the object geometry, materials, and lighting that explain the input images. Furthermore, this typically involves optimization through differentiable Monte Carlo rendering, which is brittle and computationally-expensive. In this work, we propose a simpler approach: we first relight each input image using an image diffusion model conditioned on target environment lighting and estimated object geometry. We then reconstruct a Neural Radiance Field (NeRF) with these relit images, from which we render novel views under the target lighting. We demonstrate that this strategy is surprisingly competitive and achieves state-of-the-art results on multiple relighting benchmarks. Please see our project page at https://illuminerf.github.io/.