# Deep Illumination: Approximating Dynamic Global Illumination with Generative Adversarial Network

arXiv: https://arxiv.org/abs/1710.09834 · HTML: https://mlsanity.com/paper/1710.09834/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2017.

## Built on by (most cited first, 7 of 7)

- [Neural Rendering and Its Hardware Acceleration: A Review](https://mlsanity.com/paper/2402.00028/) (2024)
- [Deep and Fast Approximate Order Independent Transparency](https://mlsanity.com/paper/2305.10197/) (2023)
- [RenderFlow: Single-Step Neural Rendering via Flow Matching](https://mlsanity.com/paper/2601.06928/) (2026)
- [Generative Adversarial Shaders for Real-Time Realism Enhancement](https://mlsanity.com/paper/2306.04629/) (2023)
- [Generative Adversarial Networks for Non-Raytraced Global Illumination on Older GPU Hardware](https://mlsanity.com/paper/2110.12039/) (2021)
- [OctaOctree Neural Radiosity for Real-time Glossy Material Rendering](https://mlsanity.com/paper/2606.08469/) (2026)
- [DiffusionShadow: Diffusion-based Shadow Caching for Neural Volume Rendering](https://mlsanity.com/paper/2609.30658/) (2026)

## Abstract (arXiv)

We present Deep Illumination, a novel machine learning technique for approximating global illumination (GI) in real-time applications using a Conditional Generative Adversarial Network. Our primary focus is on generating indirect illumination and soft shadows with offline rendering quality at interactive rates. Inspired from recent advancement in image-to-image translation problems using deep generative convolutional networks, we introduce a variant of this network that learns a mapping from Gbuffers (depth map, normal map, and diffuse map) and direct illumination to any global illumination solution. Our primary contribution is showing that a generative model can be used to learn a density estimation from screen space buffers to an advanced illumination model for a 3D environment. Once trained, our network can approximate global illumination for scene configurations it has never encountered before within the environment it was trained on. We evaluate Deep Illumination through a comparison with both a state of the art real-time GI technique (VXGI) and an offline rendering GI technique (path tracing). We show that our method produces effective GI approximations and is also computationally cheaper than existing GI techniques. Our technique has the potential to replace existing precomputed and screen-space techniques for producing global illumination effects in dynamic scenes with physically-based rendering quality.