Sketches reflect the drawing style of individual artists; therefore, it is important to consider their unique styles when extracting sketches from color images for various applications. Unfortunately, most existing sketch extraction methods are designed to extract sketches of a single style. Although there have been some attempts to generate various style sketches, the methods generally suffer from two limitations: low quality results and difficulty in training the model due to the requirement of a paired dataset. In this paper, we propose a novel multi-modal sketch extraction method that can imitate the style of a given reference sketch with unpaired data training in a semi-supervised manner. Our method outperforms state-of-the-art sketch extraction methods and unpaired image translation methods in both quantitative and qualitative evaluations.
@article{arxiv.2407.14026,
title = {Semi-supervised reference-based sketch extraction using a contrastive learning framework},
author = {Chang Wook Seo and Amirsaman Ashtari and Junyong Noh},
journal= {arXiv preprint arXiv:2407.14026},
year = {2024}
}