  Visual recognition of materials and their states is essential for
understanding the physical world, from identifying wet regions on surfaces or
stains on fabrics to detecting infected areas on plants or minerals in rocks.
Collecting data that captures this vast variability is complex due to the
scattered and gradual nature of material states. Manually annotating real-world
images is constrained by cost and precision, while synthetic data, although
accurate and inexpensive, lacks real-world diversity. This work aims to bridge
this gap by infusing patterns automatically extracted from real-world images
into synthetic data. Hence, patterns collected from natural images are used to
generate and map materials into synthetic scenes. This unsupervised approach
captures the complexity of the real world while maintaining the precision and
scalability of synthetic data. We also present the first comprehensive
benchmark for zero-shot material state segmentation, utilizing real-world
images across a diverse range of domains, including food, soils, construction,
plants, liquids, and more, each appears in various states such as wet, dry,
infected, cooked, burned, and many others. The annotation includes partial
similarity between regions with similar but not identical materials and hard
segmentation of only identical material states. This benchmark eluded top
foundation models, exposing the limitations of existing data collection
methods. Meanwhile, nets trained on the infused data performed significantly
better on this and related tasks. The dataset, code, and trained model are
available. We also share 300,000 extracted textures and SVBRDF/PBR materials to
facilitate future datasets generation.
