中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 先进控制与自动化团队
IPGAN: Identity-Preservation Generative Adversarial Network for unsupervised photo-to-caricature translation

文献类型：期刊论文

作者Yan, Lan2,3; Zheng, Wenbo1,3; Gou, Chao4; Wang, Fei-Yue3
刊名KNOWLEDGE-BASED SYSTEMS
出版日期2022-04-06
卷号241页码:11
关键词Photo-to-caricature translation Generative adversarial networks Image-to-image translation Style transfer Image warping
ISSN号0950-7051
DOI10.1016/j.knosys.2022.108223
通讯作者Gou, Chao(gouchao@mail.sysu.edu.cn)
英文摘要Photo-to-caricature translation is an extremely challenging task because there are not only texture differences between caricatures and photos, but also various spatial deformations in caricatures. Most of existing methods tend to introduce difficult obtained additional information such as precise facial landmarks to guide caricature generation. In addition, identity preservation is a crucial characteristic of caricatures, but unfortunately there seems to be few methods to consider it. Motivated by the aforementioned observations, we propose an Identity-Preservation Generative Adversarial Network (IPGAN) for unsupervised photo-to-caricature translation. In particular, considering the importance of identity retention, we propose a novel identity preservation loss to hold the identity information of original photos and improve the quality of generated caricatures. To capture realistic caricature styles, we design a style differentiation loss to help our model produce caricatures with styles that remarkably differ from photos. Moreover, to learn satisfactory deformations without supervision, our model uses a warp controller to acquire exaggerations automatically that enable to customize diverse exaggerations. As an unsupervised translation method, our IPGAN can also be applied to caricature to-photo translation. Experiments on the WebCaricature dataset suggest that our IPGAN achieves state-of-the-art performance and can generate realistic as well as identity preservation caricatures. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
WOS关键词IMAGE ; FACES
资助项目National Key R&D Program of China[2018AAA0101502] ; Key Research and Devel-opment Program of Guangzhou, China[202007050002] ; Natural Science Foundation of China[61806198] ; Natural Science Foundation of China[U1811463]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000788730900008
出版者ELSEVIER
资助机构National Key R&D Program of China ; Key Research and Devel-opment Program of Guangzhou, China ; Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/48440]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Gou, Chao
作者单位1.Xi An Jiao Tong Univ, Sch Software Engn, Xian, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
4.Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou, Peoples R China
推荐引用方式
GB/T 7714		 Yan, Lan,Zheng, Wenbo,Gou, Chao,et al. IPGAN: Identity-Preservation Generative Adversarial Network for unsupervised photo-to-caricature translation[J]. KNOWLEDGE-BASED SYSTEMS,2022,241:11.
APA Yan, Lan,Zheng, Wenbo,Gou, Chao,&Wang, Fei-Yue.(2022).IPGAN: Identity-Preservation Generative Adversarial Network for unsupervised photo-to-caricature translation.KNOWLEDGE-BASED SYSTEMS,241,11.
MLA Yan, Lan,et al."IPGAN: Identity-Preservation Generative Adversarial Network for unsupervised photo-to-caricature translation".KNOWLEDGE-BASED SYSTEMS 241(2022):11.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。