PSGAN plus plus : Robust Detail-Preserving Makeup Transfer and Removal

doi:10.1109/TPAMI.2021.3083484

CORC > 自动化研究所 > 中国科学院自动化研究所 > 智能感知与计算研究中心

	PSGAN plus plus : Robust Detail-Preserving Makeup Transfer and Removal
	Liu, Si 2; Jiang, Wentao 2; Gao, Chen 2; He, Ran 3; Feng, Jiashi 4; Li, Bo 2; Yan, Shuicheng 1
刊名	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
	2022-11-01
卷号	44 期号:11 页码:8538-8551
关键词	Faces Generative adversarial networks Task analysis Visualization Nose Image resolution Skin Makeup transfer makeup removal generative adversarial networks
ISSN号	0162-8828
DOI	10.1109/TPAMI.2021.3083484
通讯作者	He, Ran(ran.he@ia.ac.cn)
英文摘要	In this paper, we address the makeup transfer and removal tasks simultaneously, which aim to transfer the makeup from a reference image to a source image and remove the makeup from the with-makeup image respectively. Existing methods have achieved much advancement in constrained scenarios, but it is still very challenging for them to transfer makeup between images with large pose and expression differences, or handle makeup details like blush on cheeks or highlight on the nose. In addition, they are hardly able to control the degree of makeup during transferring or to transfer a specified part in the input face. These defects limit the application of previous makeup transfer methods to real-world scenarios. In this work, we propose a Pose and expression robust Spatial-aware GAN (abbreviated as PSGAN++). PSGAN++ is capable of performing both detail-preserving makeup transfer and effective makeup removal. For makeup transfer, PSGAN++ uses a Makeup Distill Network (MDNet) to extract makeup information, which is embedded into spatial-aware makeup matrices. We also devise an Attentive Makeup Morphing (AMM) module that specifies how the makeup in the source image is morphed from the reference image, and a makeup detail loss to supervise the model within the selected makeup detail area. On the other hand, for makeup removal, PSGAN++ applies an Identity Distill Network (IDNet) to embed the identity information from with-makeup images into identity matrices. Finally, the obtained makeup/identity matrices are fed to a Style Transfer Network (STNet) that is able to edit the feature maps to achieve makeup transfer or removal. To evaluate the effectiveness of our PSGAN++, we collect a Makeup Transfer In the Wild (MT-Wild) dataset that contains images with diverse poses and expressions and a Makeup Transfer High-Resolution (MT-HR) dataset that contains high-resolution images. Experiments demonstrate that PSGAN++ not only achieves state-of-the-art results with fine makeup details even in cases of large pose/expression differences but also can perform partial or degree-controllable makeup transfer. Both the code and the newly collected datasets will be released at https://github.com/wtjiang98/PSGAN.
资助项目	National Natural Science Foundation of China[61876177] ; Beijing Natural Science Foundation[4202034] ; Beijing Natural Science Foundation[JQ18017] ; Zhejiang Laboratory[2019KD0AB04]
WOS关键词	NETWORK
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE COMPUTER SOC
WOS记录号	WOS:000864325900088
资助机构	National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Zhejiang Laboratory
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50296]
专题	自动化研究所_智能感知与计算研究中心
通讯作者	He, Ran
作者单位	1.Sea AI Lab SAIL, Singapore 117576, Singapore 2.Beihang Univ, Inst Artificial Intelligence, Beijing 100083, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100049, Peoples R China 4.Natl Univ Singapore, Singapore 119077, Singapore
推荐引用方式 GB/T 7714	Liu, Si,Jiang, Wentao,Gao, Chen,et al. PSGAN plus plus : Robust Detail-Preserving Makeup Transfer and Removal[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2022,44(11):8538-8551.
APA	Liu, Si.,Jiang, Wentao.,Gao, Chen.,He, Ran.,Feng, Jiashi.,...&Yan, Shuicheng.(2022).PSGAN plus plus : Robust Detail-Preserving Makeup Transfer and Removal.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,44(11),8538-8551.
MLA	Liu, Si,et al."PSGAN plus plus : Robust Detail-Preserving Makeup Transfer and Removal".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 44.11(2022):8538-8551.