Reference-guided structure-aware deep sketch colorization for cartoons  

作　　者：Xueting Liu Wenliang Wu Chengze Li Yifan Li Huisi Wu 

机构地区：[1]Caritas Institute of Higher Education,Hong Kong SAR,China [2]Shenzhen University,Shenzhen 518060,China

出　　处：《Computational Visual Media》2022年第1期135-148,共14页计算可视媒体（英文版）

基　　金：supported in part by a CIHE Institutional Development Grant No.IDG200107;the National Natural Science Foundation of China under Grant No.61973221;the Natural Science Foundation of Guangdong Province of China under Grant Nos.2018A030313381 and 2019A1515011165.

摘　　要：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.

关 键 词：sketch colorization image style editing deep feature understanding reference-based image colorization 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]