Synthetic Texture Datasets: Challenges, Creation, and Curation

Texture data serves as a valuable tool for interpreting the high-level features models learn, uncovering biases, and identifying security vulnerabilities. However, works in this space have been limited by small texture datasets and synthesis methods that struggle to scale in the diversity and specificity required for these tasks. In this work, we introduce an extensible methodology for generating high-quality, diverse texture images, which we use to create the Prompted Textures Dataset (PTD), a new texture dataset spanning 246,285 images across 56 texture classes. Our comparison against real texture data demonstrates that PTD is more diverse while maintaining quality. Additionally, human evaluations confirm that every stage in our methodology enhances texture quality, yielding a 3.4% increase in quality and a 4.5% increase in representativeness overall. Our dataset is available for download at https://zenodo.org/records/15359142.