Portrait caricature based on double-stream cycle mapping network

Portrait caricature based on double-stream cycle mapping network
Kong Fanmin1
Pu Yuanyuan1,2
Zhao Zhengpeng1
Deng Xin1
Yang Qiuxia1
1. School of Information Science & Engineering, Yunnan University, Kunming 650504, China
2. University Key Laboratory of Internet of Things Technology & Application of Yunnan Province, Kunming 650504, China

摘要

Portrait artistic style transfer aims to transfer the style from a given reference artistic portrait painting to a portrait photo while preserving the basic semantic structure of the person's face. However, due to the sensitivity of the human visual system to the facial structure of person, the task of artistic style transfer of portraits is often more challenging than that for general image, especially for caricature type which with more abstract style elements. Existing image style transfer methods, which do not consider the abstraction of the caricature style and the preservation of basic semantic structure of the portrait face, often suffer from serious structural collapse and feature information confusion when applied to the portrait caricature task. To address this problem, this paper proposed a double-stream cycle mapping DSCM(double-stream cycle mapping network) network to portrait caricature. Firstly, based on BeautyGAN, it introduced a structural consistency loss and cooperating with the cycle consistency loss to maintain the integrity of the overall semantic structure of the portrait. Secondly, it designed a feature encoder combined with U2-Net to capture more valuable feature information of input images at different scales. In addition, it further introduced a style discriminator to discriminate the encoded style features to assist the network in learning abstract caricature style features closer to the target image. The experiments conducted qualitative comparisons of five advanced methods, and quantitative comparisons of FID(Fréchet inception distance) and PSNR(peak signal to noise ratio) index scores. The experimental results show that this method is superior to other methods. Through extensive experimental verification, the portrait caricature obtained by this method not only maintains the overall structure of the portrait and the basic semantic structure of the face, but also fully learns the abstract style of caricature.

基金项目

国家自然科学基金资助项目(62162068,61271361,61761046,62061049)
云南省应用基础研究面上项目(2018FB100)
云南省科技厅应用基础研究计划重点项目(202001BB050043,2019FA044)

出版信息

DOI: 10.19734/j.issn.1001-3695.2023.05.0226
出版期卷: 《计算机应用研究》 Printed Article, 2023年第40卷 第12期
所属栏目: Technology of Graphic & Image
出版页码: 3854-3858
文章编号: 1001-3695(2023)12-053-3854-05

发布历史

[2023-08-14] Accepted Paper
[2023-12-05] Printed Article

引用本文

孔凡敏, 普园媛, 赵征鹏, 等. 基于双流循环映射网络的肖像漫画化 [J]. 计算机应用研究, 2023, 40 (12): 3854-3858. (Kong Fanmin, Pu Yuanyuan, Zhao Zhengpeng, et al. Portrait caricature based on double-stream cycle mapping network [J]. Application Research of Computers, 2023, 40 (12): 3854-3858. )

关于期刊

  • 计算机应用研究 月刊
  • Application Research of Computers
  • 刊号 ISSN 1001-3695
    CN  51-1196/TP

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