Translation of Illustration Artist Style Using Sailormoonredraw Data

Abstract

The decision to draw requires answers to two questions: what to draw and how to draw. The latter refers to artist style and is an important factor in creating any illustration. In this paper, we propose a novel task, artist style translation, which translates one artist’s illustration into that of another artist style using deep learning. To solve this task in a supervised manner, we created a novel illustration dataset, SailormoonDataset, which consists of more than 2,000 artist’s stylistic illustrations of the same content. In addition, we propose a method based on the Swapping Autoencoder by introducing a new loss function for supervised learning and using multiple images to represent an artist style. We translate the face illustration of Sailor Moon into styles of different artists. By comparing the current results to those of the Swapping Autoencoder, we find that the proposed method successfully achieves the artist style translation.