Subias, Jose DanielDaniel-Soriano, SaúlGutierrez, DiegoSerrano, AnaWang, BeibeiWilkie, Alexander2025-06-202025-06-2020251467-8659https://doi.org/10.1111/cgf.70182https://diglib.eg.org/handle/10.1111/cgf70182Large diffusion models have made a remarkable leap synthesizing high-quality artistic images from text descriptions. However, these powerful pre-trained models still lack control to guide key material appearance properties, such as gloss. In this work, we present a threefold contribution: (1) we analyze how gloss is perceived across different artistic styles (i.e., oil painting, watercolor, ink pen, charcoal, and soft crayon); (2) we leverage our findings to create a dataset with 1,336,272 stylized images of many different geometries in all five styles, including automatically-computed text descriptions of their appearance (e.g., ''A glossy bunny hand painted with an orange soft crayon''); and (3) we train ControlNet to condition Stable Diffusion XL synthesizing novel painterly depictions of new objects, using simple inputs such as edge maps, hand-drawn sketches, or clip arts. Compared to previous approaches, our framework yields more accurate results despite the simplified input, as we show both quantitative and qualitatively.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Non-photorealistic rendering; Image processing; PerceptionComputing methodologies → Nonphotorealistic renderingImage processingPerception10.1111/cgf.7018215 pages