Image style disentangling for instance-level facial attribute transfer

doi:10.1016/j.cviu.2021.103205

	Image style disentangling for instance-level facial attribute transfer
	Guo, Xuyang 2,3; Kan, Meina 3; He, Zhenliang 2,3; Song, Xingguang 1; Shan, Shiguang 3
刊名	COMPUTER VISION AND IMAGE UNDERSTANDING
	2021-06-01
卷号	207 页码:10
关键词	Instance-level facial attribute transfer Image to image translation Generative adversarial network Weakly supervised style learning
ISSN号	1077-3142
DOI	10.1016/j.cviu.2021.103205
英文摘要	Instance-level facial attribute transfer aims at transferring an attribute including its style from a source face to a target one. Existing studies have limitations on fidelity or correctness. To address this problem, we propose a weakly supervised style disentangling method embedded in Generative Adversarial Network (GAN) for accurate instance-level attribute transfer, using only binary attribute annotations. In our method, the whole attributes transfer process is designed as two steps for easier transfer, which first removes the original attribute or transfers it to a neutral state and then adds the attributes style disentangled from a source face. Moreover, a style disentangling module is proposed to extract the attribute style of an image used in the adding step. Our method aims for accurate attribute style transfer. However, it is also capable of semantic attribute editing as a special case, which is not achievable with existing instance-level attribute transfer methods. Comprehensive experiments on CelebA Dataset show that our method can transfer the style more precisely than existing methods, with an improvement of 39% in user study, 16.5% in accuracy, and about 3.3 in FID.
资助项目	National Key R&D Program of China[2017YFA0700800] ; National Key R&D Program of China[Y808401] ; Natural Science Foundation of China[61772496]
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	ACADEMIC PRESS INC ELSEVIER SCIENCE
WOS记录号	WOS:000648965900004
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/17776]
专题	中国科学院计算技术研究所
通讯作者	Kan, Meina
作者单位	1.Huawei Technol Co Ltd, Shenzhen 518129, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Guo, Xuyang,Kan, Meina,He, Zhenliang,et al. Image style disentangling for instance-level facial attribute transfer[J]. COMPUTER VISION AND IMAGE UNDERSTANDING,2021,207:10.
APA	Guo, Xuyang,Kan, Meina,He, Zhenliang,Song, Xingguang,&Shan, Shiguang.(2021).Image style disentangling for instance-level facial attribute transfer.COMPUTER VISION AND IMAGE UNDERSTANDING,207,10.
MLA	Guo, Xuyang,et al."Image style disentangling for instance-level facial attribute transfer".COMPUTER VISION AND IMAGE UNDERSTANDING 207(2021):10.