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Bibliographic Details
Main Authors: Meleti, Uma, Huang, Siyu
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2603.07776
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author Meleti, Uma
Huang, Siyu
author_facet Meleti, Uma
Huang, Siyu
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2603_07776
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Parameterized Brushstroke Style Transfer
Meleti, Uma
Huang, Siyu
Computer Vision and Pattern Recognition
Graphics
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.
title Parameterized Brushstroke Style Transfer
topic Computer Vision and Pattern Recognition
Graphics
url https://arxiv.org/abs/2603.07776