Reference-guided structure-aware deep sketch colorization for cartoons
Electronic computers. Computer science
sketch colorization
0202 electrical engineering, electronic engineering, information engineering
image style editing
reference-based image colorization
QA75.5-76.95
02 engineering and technology
deep feature understanding
DOI:
10.1007/s41095-021-0228-6
Publication Date:
2021-10-27T16:03:16Z
AUTHORS (5)
ABSTRACT
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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