Improving The Quality Of Illustrations: Transforming Amateur Illustrations To A Professional Standard

Abstract

We propose an amateur-to professional-level illustration translator that can modify amateur illustrations slightly to produce professional-level quality images. The proposed translator is a GAN-based image translation module. We focus only on the neighboring region of a contour to improve the quality of the illustration by applying image completion to the neighboring region of the extracted line drawing. We artificially augment amateur-level illustrations from professional-level illustrations to solve the lack of a pair of amateur-level and professional-level datasets. This enables us to automatically prepare a pair of amateur-level and professional-level images, through which we can train a translator network. Through experiments and user study, we show that the proposed method improves the quality of amateur illustrations.

GCN-Based Multi-Modal Multi-Label Attribute Classification in Anime Illustration Using Domain-Specific Semantic Features

“…With the development of the animation industry in recent years, several studies related to anime illustration such as illustration editing [1,2], line-art colorization [3,4] and cartoon face generation [5,6] have been conducted. Due to the increasing number of anime illustrations, there is a growing need for techniques to classify them, and these techniques have a potential to enhance the above various studies.…”