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# General
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VasTexture is a
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The repository contains 500,000
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The PBR materials and textures were extracted from natural images using an unsupervised statistical approach (no human intervention).
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As a result, the textures and PBR materials are significantly more diverse but less refined compared to assets made using manual and AI approaches.
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This dataset is more suitable for task needing large number of highly diverse assets like building datasets or large scale procedural generation.
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---
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# General
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VasTexture is a large-scale dataset of textures and PBR materials extracted from real-world images.
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The repository contains 500,000 highly diverse texture images and PBR materials. All assets are free to download and use for any purpose (CC0 license).
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The dataset is divided into textures images, and PBR materials. Where texture image are simply crop of regions in images with uniform textures.
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The PBR materials and textures were extracted from natural images using an unsupervised statistical approach (no human intervention).
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As a result, the textures and PBR materials are significantly more diverse but less refined compared to assets made using manual and AI approaches. This dataset is more suitable for tasks needing a large number of highly diverse assets like building datasets or large scale procedural generation.
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## [Project Website](https://sites.google.com/view/infinitexture/home)
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## File Structure
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The dataset is composed into two assets types textures images and PBR materials, each file contain between 5,000 to 40,000 assets
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Texture image files contain the world **Texture** in the file. If the textures are seamless/tilable, the world **seamless** will appear in the file name. If the texture is 512x512 or larger, the world **large** will appear in the file name.
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PBR materiasl files contain the world **PBR** in the file. If the PBR are seamless/tilable, the world **seamless** will appear in the file name. If the PBR is 512x512 or larger, the world **large** will appear in the file name.
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Note that Seamless texture images have been modified compare to the original image crop
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## Data generation code:
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The Python scripts used to extract these assets are supplied at:
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Texture_And_Material_ExtractionCode_And_Documentation.zip
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The code could be run in any folder of random images extract regions with uniform textures and turn these into PBR materials.
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Code for transforming data to seamless available at [https://github.com/sagieppel/convert-image-into-seamless-tileable-texture](https://github.com/sagieppel/convert-image-into-seamless-tileable-texture)
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# GITHUB and Alternative download sources:
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GitHub: [Texture/PBR extraction](https://github.com/sagieppel/Unsupervised-extraction-of-textures-and-PBR-materials-from-images), [Texture To Seamless](https://github.com/sagieppel/convert-image-into-seamless-tileable-texture)
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https://sites.google.com/view/infinitexture/home
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https://zenodo.org/records/12629301
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## Papers
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Main paper:
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[Infusing Synthetic Data with Real-World Patterns for
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Zero-Shot Material State Segmentation](https://proceedings.neurips.cc/paper_files/paper/2024/file/6ef4a4b387a5a547ea699f3df7fc1248-Paper-Datasets_and_Benchmarks_Track.pdf)
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More detailed:
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[Vastextures: Vast repository of textures and PBR materials
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extracted from real-world images using unsupervised methods](https://arxiv.org/pdf/2406.17146)
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