Abstract
Digital cartoon production requires extensive manual labor to colorize sketches with visually pleasant color composition and color shading. During colorization, the artist usually takes an existing cartoon image as color guidance, particularly when colorizing related characters or an animation sequence. Reference-guided colorization is more intuitive than colorization with other hints, such as color points or scribbles, or text-based hints. Unfortunately, reference-guided colorization is challenging since the style of the colorized image should match the style of the reference image in terms of both global color composition and local color shading. In this paper, we propose a novel learning-based framework which colorizes a sketch based on a color style feature extracted from a reference color image. Our framework contains a color style extractor to extract the color feature from a color image, a colorization network to generate multi-scale output images by combining a sketch and a color feature, and a multi-scale discriminator to improve the reality of the output image. Extensive qualitative and quantitative evaluations show that our method outperforms existing methods, providing both superior visual quality and style reference consistency in the task of reference-based colorization.
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Acknowledgements
This work was supported in part by a CIHE Institutional Development Grant No. IDG200107, the National Natural Science Foundation of China under Grant No. 61973221, and the Natural Science Foundation of Guangdong Province of China under Grant Nos. 2018A030313381 and 2019A1515011165.
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Xueting Liu received her B.Eng. degree in computer science and technology from Tsinghua University and her Ph.D. degree in computer science and engineering from the Chinese University of Hong Kong in 2009 and 2014, respectively. She is currently an assistant professor in the School of Computing and Information Sciences, Caritas Institute of Higher Education. Her research interests include computational art, intelligent art, computer vision, and computer graphics.
Wenliang Wu received his B.Sc. degree from Guangdong Ocean University of Science and Technology in 2019. He is currently a graduate student in the College of Computer Science and Software Engineering, Shenzhen University. His research interests include computer vision and computer graphics.
Chengze Li received her B.Eng. degree from the University of Science and Technology of China in 2013, and her Ph.D. degree in computer science and engineering from the Chinese University of Hong Kong in 2020. Chengze is currently an assistant professor in the School of Computing and Information Sciences, Caritas Institute of Higher Education, with research interests in 2D non-photorealistic media analysis and processing, computational photography, and computer graphics.
Yifan Li received his B.Sc. degree from Jiangxi University of Science and Technology in 2018 and is now a graduate student in the College of Computer Science and Software Engineering, Shenzhen University. His research interests include computer graphics, computer vision, machine learning, and deep learning.
Huisi Wu received his B.E. and M.E. degrees in computer science both from Xi’an Jiaotong University in 2004 and 2007, respectively. He obtained his Ph.D. degree in computer science from the Chinese University of Hong Kong in 2011. He is currently an associate professor in the College of Computer Science and Software Engineering, Shenzhen University. His research interests include computer graphics, image processing, and medical imaging.
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Liu, X., Wu, W., Li, C. et al. Reference-guided structure-aware deep sketch colorization for cartoons. Comp. Visual Media 8, 135–148 (2022). https://doi.org/10.1007/s41095-021-0228-6
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DOI: https://doi.org/10.1007/s41095-021-0228-6