Material Palette: Extraction of Materials from a Single Image

Ivan Lopes, Fabio Pizzati, Raoul de Charette; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 4379-4388

Physically-Based Rendering (PBR) is key to modeling the interaction between light and materials and finds extensive applications across computer graphics domains. However acquiring PBR materials is costly and requires special apparatus. In this paper we propose a method to extract PBR materials from a single real-world image. We do so in two steps: first we map regions of the image to material concept tokens using a diffusion model allowing the sampling of texture images resembling each material in the scene. Second we leverage a separate network to decompose the generated textures into spatially varying BRDFs (SVBRDFs) offering us readily usable materials for rendering applications. Our approach relies on existing synthetic material libraries with SVBRDF ground truth. It 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. Along with video we share code and models as open-source on the project page: https://github.com/astra-vision/MaterialPalette