Stroke Patches: Customizable Artistic Image Styling Using Regression

Ian Jaffray, John F Bronskill

Track: Paper

Keywords: image styling, artistic effects

TL;DR: We present a regression-based method for artistically styling images that allows for explicit control over the stroke composition and level of detail in the rendered image through the use of stroke patches.

Abstract: We present a novel, regression-based method for artistically styling images. Unlike recent neural style transfer or diffusion-based approaches, our method allows for explicit control over the stroke composition and level of detail in the rendered image through the use of an extensible set of stroke patches. The stroke patch sets are procedurally generated by small programs that control the shape, size, orientation, density, color, and noise level of the strokes in the individual patches. Once trained on a set of stroke patches, a U-Net based regression model can render any input image in a variety of distinct, evocative and customizable styles.

Submission Number: 19