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Semi-supervised reference-based sketch extraction using a contrastive learning framework
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DOI：10.1145/3592392.png)
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摘要
En 中文
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.
Keyword:
Sketch-extraction
Auto-colorization
Image-to-image translation
期刊
IF:
9.5
论文数:
4.7K
被引数:
3.6W
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