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A painter is free to modify how components of a natural scene are depicted,which can lead to a perceptually convincing image of the distal world. Thissignals a major difference between photos and paintings: paintings areexplicitly created for human perception. Studying these painterly depictionscould be beneficial to a multidisciplinary audience. In this paper, we captureand explore the painterly depictions of materials to enable the study ofdepiction and perception of materials through the artists' eye. We annotated adataset of 19k paintings with 200k+ bounding boxes from which polygon segmentswere automatically extracted. Each bounding box was assigned a coarse label(e.g., fabric) and a fine-grained label (e.g., velvety, silky). We demonstratethe cross-disciplinary utility of our dataset by presenting novel findingsacross art history, human perception, and computer vision. Our experimentsinclude analyzing the distribution of materials depicted in paintings, showinghow painters create convincing depictions using a stylized approach, anddemonstrating how paintings can be used to build more robust computer visionmodels. We conclude that our dataset of painterly material depictions is a richsource for gaining insights into the depiction and perception of materialsacross multiple disciplines. The MIP dataset is freely accessible athttps://materialsinpaintings.tudelft.nl

Materials In Paintings (MIP): An interdisciplinary dataset for perception, art history, and computer vision