Semi-supervised reference-based sketch extraction using a contrastive learning framework

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. Althoug...

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Bibliographic Details
Published in:arXiv.org 2024-07
Main Authors: Seo, Chang Wook, Ashtari, Amirsaman, Noh, Junyong
Format: Article
Language:English
Subjects:
Artists
Color imagery
Image quality
Qualitative analysis
Sketches
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Summary: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.
ISSN:2331-8422