Vastextures: Vast repository of textures and PBR materials extracted from real-world images using unsupervised methods
Sagi Eppel

TL;DR
Vastextures is a large, diverse, and automatically extracted repository of real-world textures and PBR materials, designed to support AI training and virtual world creation with less refined but highly varied assets.
Contribution
The paper introduces Vastextures, a massive unsupervised method for extracting diverse real-world textures and PBR materials from natural images, expanding asset availability for AI and virtual worlds.
Findings
Outperforms existing repositories in AI training tasks
Contains 500,000 diverse textures and materials
Includes emergent properties in extracted PBR materials
Abstract
Vastextures is a vast repository of 500,000 textures and PBR materials extracted from real-world images using an unsupervised process. The extracted materials and textures are extremely diverse and cover a vast range of real-world patterns, but at the same time less refined compared to existing repositories. The repository is composed of 2D textures cropped from natural images and SVBRDF/PBR materials generated from these textures. Textures and PBR materials are essential for CGI. Existing materials repositories focus on games, animation, and arts, that demand a limited amount of high-quality assets. However, virtual worlds and synthetic data are becoming increasingly important for training A.I systems for computer vision. This application demands a huge amount of diverse assets but at the same time less affected by noisy and unrefined assets. Vastexture aims to address this need by…
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Taxonomy
TopicsImage Processing and 3D Reconstruction
MethodsFocus
