Material Palette: Extraction of Materials from a Single Image

TL;DR

This paper introduces a novel method for extracting physically-based rendering materials from a single real-world image by combining diffusion models and neural decomposition, enabling realistic material editing and application in rendering.

Contribution

It presents a new approach that uses diffusion models and neural networks to extract and decompose materials from a single image, with unsupervised domain adaptation for better generalization.

Findings

Effective extraction of PBR materials from real images.

Successful application to editing 3D scenes with real-world materials.

Open-source code and models available for reproducibility.

Abstract

In this paper, we propose a method to extract physically-based rendering (PBR) materials from a single real-world image. We do so in two steps: first, we map regions of the image to material concepts using a diffusion model, which allows the sampling of texture images resembling each material in the scene. Second, we benefit from a separate network to decompose the generated textures into Spatially Varying BRDFs (SVBRDFs), providing us with materials ready to be used in rendering applications. Our approach builds on existing synthetic material libraries with SVBRDF ground truth, but also exploits a diffusion-generated RGB texture dataset to allow generalization to new samples using unsupervised domain adaptation (UDA). Our contributions are thoroughly evaluated on synthetic and real-world datasets. We further demonstrate the applicability of our method for editing 3D scenes with materials estimated from real photographs. The code and models will be made open-source. Project page: https://astra-vision.github.io/MaterialPalette/