Semi-supervised reference-based sketch extraction using a contrastive learning framework
| Autor: | Seo, Chang Wook, Ashtari, Amirsaman, Noh, Junyong |
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| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | ACM Transactions on Graphics (TOG) 2023, Volume 42, Issue 4 Article No.: 56, Pages 1 - 12 |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Main paper 1-12 page, Supplementary 13-34 page |
| Databáze: | arXiv |
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