自动化学报2018，Vol.44Issue(5)：840-854,15.DOI:10.16383/j.aas.2018.c170486
基于生成对抗网络的漫画草稿图简化
Sketch Simplification Using Generative Adversarial Networks
摘要
Abstract
Sketch simplification is a critical part of cartoon drawing. To some extent,the existing approaches already have basic ability of sketch cleanup,but still have limitation in some situations because of the diversity of sketch drawing methods and complexity of sketch contents. In this paper,we present a novel approach of building a model for sketch simplification, which is based on the conditional random field(CRF)and least squares generative adversarial networks(LSGAN).Through the zero-sum game of generator and discriminator in the model and the learning restriction of conditional random field, we can obtain simplified images more similar to standard clean images. At the same time, we build a dataset containing a large number of pairs of sketches and clean images in different painting ways and contents. Finally, experiments show that our approach can obtain better results than the state of the art approach for sketch simplification.关键词
草图简化/最小二乘生成式对抗网络/深度学习/条件随机场Key words
Sketch simplification/least squares generative adversarial network (LSGAN)/deep learning/conditional random field(CRF)引用本文复制引用
卢倩雯,陶青川,赵娅琳,刘蔓霄..基于生成对抗网络的漫画草稿图简化[J].自动化学报,2018,44(5):840-854,15.
