Parameterized Brushstroke Style Transfer

TL;DR

This paper introduces a style transfer method that operates in the brush stroke domain rather than the pixel domain, resulting in more natural and visually appealing artistic images.

Contribution

It proposes a novel style transfer approach that models images as brush strokes, improving visual quality over traditional pixel-based methods.

Findings

Brush stroke domain transfer yields more natural images.

The method outperforms pixel-based style transfer in visual quality.

Enhanced artistic representation with better style fidelity.

Abstract

Computer Vision-based Style Transfer techniques have been used for many years to represent artistic style. However, most contemporary methods have been restricted to the pixel domain; in other words, the style transfer approach has been modifying the image pixels to incorporate artistic style. However, real artistic work is made of brush strokes with different colors on a canvas. Pixel-based approaches are unnatural for representing these images. Hence, this paper discusses a style transfer method that represents the image in the brush stroke domain instead of the RGB domain, which has better visual improvement over pixel-based methods.