# ePBR: Extended PBR Materials in Image Synthesis

arXiv: https://arxiv.org/abs/2504.17062 · HTML: https://mlsanity.com/paper/2504.17062/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 55 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 28 of 28)

- [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)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [Diffusion Model-Based Image Editing: A Survey](https://mlsanity.com/paper/2402.17525/) (2024)
- [Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image](https://mlsanity.com/paper/1905.02722/) (2019)
- [Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction](https://mlsanity.com/paper/2409.18124/) (2024)
- [RGBleftrightarrowX: Image decomposition and synthesis using material- and lighting-aware diffusion models](https://mlsanity.com/paper/2405.00666/) (2024)
- [StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal](https://mlsanity.com/paper/2406.16864/) (2024)
- [Single-Image SVBRDF Capture with a Rendering-Aware Deep Network](https://mlsanity.com/paper/1810.09718/) (2018)
- [Learning-based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracing](https://mlsanity.com/paper/2211.03017/) (2022)
- [Intrinsic Image Diffusion for Indoor Single-view Material Estimation](https://mlsanity.com/paper/2312.12274/) (2023)
- [Meta 3D AssetGen: Text-to-Mesh Generation with High-Quality Geometry, Texture, and PBR Materials](https://mlsanity.com/paper/2407.02445/) (2024)
- [DiffusionLight: Light Probes for Free by Painting a Chrome Ball](https://mlsanity.com/paper/2312.09168/) (2023)
- [DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models](https://mlsanity.com/paper/2501.18590/) (2025)
- [Intrinsic Image Decomposition via Ordinal Shading](https://mlsanity.com/paper/2311.12792/) (2023)
- [DreamMat: High-quality PBR Material Generation with Geometry- and Light-aware Diffusion Models](https://mlsanity.com/paper/2405.17176/) (2024)
- [Colorful Diffuse Intrinsic Image Decomposition in the Wild](https://mlsanity.com/paper/2409.13690/) (2024)
- [Alchemist: Parametric Control of Material Properties with Diffusion Models](https://mlsanity.com/paper/2312.02970/) (2023)
- [MaterialGAN: Reflectance Capture using a Generative SVBRDF Model](https://mlsanity.com/paper/2010.00114/) (2020)
- [Material Anything: Generating Materials for Any 3D Object via Diffusion](https://mlsanity.com/paper/2411.15138/) (2024)
- [Boosting 3D Object Generation through PBR Materials](https://mlsanity.com/paper/2411.16080/) (2024)
- [Materialist: Physically Based Editing Using Single-Image Inverse Rendering](https://mlsanity.com/paper/2501.03717/) (2025)
- [MCMat: Multiview-Consistent and Physically Accurate PBR Material Generation](https://mlsanity.com/paper/2412.14148/) (2024)
- [SpongeCake: A Layered Microflake Surface Appearance Model](https://mlsanity.com/paper/2110.07145/) (2021)
- [Single-image Reflectance and Transmittance Estimation from Any Flatbed Scanner](https://mlsanity.com/paper/2502.14462/) (2025)

## Built on by (most cited first, 3 of 3)

- [MatSpray: Fusing 2D Material World Knowledge on 3D Geometry](https://mlsanity.com/paper/2512.18314/) (2025)
- [SIRR-LMM: Single-image Reflection Removal via Large Multimodal Model](https://mlsanity.com/paper/2601.07209/) (2026)
- [PhysEditBench: A Protocol-Conditioned Benchmark for Dense Physical-Map Prediction with Image Editors](https://mlsanity.com/paper/2605.13493/) (2026)

## Abstract (arXiv)

Realistic indoor or outdoor image synthesis is a core challenge in computer vision and graphics. The learning-based approach is easy to use but lacks physical consistency, while traditional Physically Based Rendering (PBR) offers high realism but is computationally expensive. Intrinsic image representation offers a well-balanced trade-off, decomposing images into fundamental components (intrinsic channels) such as geometry, materials, and illumination for controllable synthesis. However, existing PBR materials struggle with complex surface models, particularly high-specular and transparent surfaces. In this work, we extend intrinsic image representations to incorporate both reflection and transmission properties, enabling the synthesis of transparent materials such as glass and windows. We propose an explicit intrinsic compositing framework that provides deterministic, interpretable image synthesis. With the Extended PBR (ePBR) Materials, we can effectively edit the materials with precise controls.