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Recurrent Generative Adversarial Network for Face Completion
Wang Q(王强)1,2,3; Fan HJ(范慧杰)1,2; Sun G(孙干)1,2,3; Ren WH(任卫红)1,2,3; Tang YD(唐延东)1,2
Department机器人学研究室
Source PublicationIEEE TRANSACTIONS ON MULTIMEDIA
ISSN1520-9210
2021
Volume23Pages:429-442
Indexed BySCI
WOS IDWOS:000601877600034
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61873259, 61821005] ; Cooperation Projects of CAS ITRI [CAS-ITRI201905] ; Key Research and Development Program of Liaoning [2019JH2/10100014]
KeywordFace Feature extraction Recurrent neural networks Generative adversarial networks Semantics Image restoration Gallium nitride Recurrent neural network generative adversarial network face completion short link
Abstract

Most recently-proposed face completion algorithms use high-level features extracted from convolutional neural networks (CNNs) to recover semantic texture content. Although the completed face is natural-looking, the synthesized content still lacks lots of high-frequency details, since the high-level features cannot supply sufficient spatial information for details recovery. To tackle this limitation, in this paper, we propose a Recurrent Generative Adversarial Network (RGAN) for face completion. Unlike previous algorithms, RGAN can take full advantage of multi-level features, and further provide advanced representations from multiple perspectives, which can well restore spatial information and details in face completion. Specifically, our RGAN model is composed of a CompletionNet and a DisctiminationNet, where the CompletionNet consists of two deep CNNs and a recurrent neural network (RNN). The first deep CNN is presented to learn the internal regulations of a masked image and represent it with multi-level features. The RNN model then exploits the relationships among the multi-level features and transfers these features in another domain, which can be used to complete the face image. Benefiting from bidirectional short links, another CNN is used to fuse multi-level features transferred from RNN and reconstruct the face image in different scales. Meanwhile, two context discrimination networks in the DisctiminationNet are adopted to ensure the completed image consistency globally and locally. Experimental results on benchmark datasets demonstrate qualitatively and quantitatively that our model performs better than the state-of-the-art face completion models, and simultaneously generates realistic image content and high-frequency details. The code will be released available soon.

Language英语
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28143
Collection机器人学研究室
Corresponding AuthorFan HJ(范慧杰)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.University of Chinese Academy of Sciences, Huairou 100049, China
Recommended Citation
GB/T 7714
Wang Q,Fan HJ,Sun G,et al. Recurrent Generative Adversarial Network for Face Completion[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2021,23:429-442.
APA Wang Q,Fan HJ,Sun G,Ren WH,&Tang YD.(2021).Recurrent Generative Adversarial Network for Face Completion.IEEE TRANSACTIONS ON MULTIMEDIA,23,429-442.
MLA Wang Q,et al."Recurrent Generative Adversarial Network for Face Completion".IEEE TRANSACTIONS ON MULTIMEDIA 23(2021):429-442.
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