One-shot Line Extraction from Color Illustrations

Sketch colorization has been explored extensively using deep learning-based approaches. However, there are few works on the inverse problem of extracting lines from color illustrations, which is challenging due to the abstract illustration in cartoon rendering and the diversity of colorization style. In this paper, we propose a learning-based framework to explore one-shot line extraction from color illustrations. To avoid over-fitting, we simplified the proposed model and proposed a method for data augmentation when training using a single “color illustration-line drawing” pair. Then, we verified the model's effectiveness and efficiency in a group of color illustrations with similar colorization style. The experiments showed that the proposed, our method can outperform the conventional CNN (convolution neural network)-based sketch extraction methods.