Artist‐Inator: Text‐based, Gloss‐aware Non‐photorealistic Stylization
J. Daniel Subías, Saul Daniel‐Soriano, Diego Gutiérrez, Ana Serrano · Computer Graphics Forum · 2025
Abstract Large 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.