CASIA OpenIR  > 智能感知与计算研究中心
Geometry Guided Adversarial Facial Expression Synthesis
Lingxiao Song1; Zhihe Lu1,2,3; Ran He1,2,3; Zhenan Sun1,2,3; Tieniu Tan1,2,3
2018-10-22
Conference NameACM Multimedia Conference
Conference Date2018.10.22-2018.10.26
Conference PlaceSeoul, Korea
Abstract

Facial expression synthesis has drawn much attention in the feld of computer graphics and pattern recognition. It has been widely used in face animation and recognition. However, it is still challenging due to the high-level semantic presence of large and non-linear face geometry variations. This paper proposes a Geometry-Guided Generative Adversarial Network (G2-GAN) for continuously-adjusting and identity-preserving facial expression synthesis. We employ facial geometry (fducial points) as a controllable condition to guide facial texture synthesis with specifc expression. A pair of generative adversarial subnetworks is jointly trained towards opposite tasks: expression removal and expression synthesis. The paired networks form a mapping cycle between neutral expression and arbitrary expressions, with which the proposed approach can be conducted among unpaired data. The proposed paired networks also facilitate other applications such as face transfer, expression interpolation and expression-invariant face recognition. Experimental results on several facial expression databases show that our method can generate compelling perceptual results on different expression editing tasks.

KeywordFacial Expression Synthesis Generative Adversarial Networks Unpaired Image-to-image Transformation
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23533
Collection智能感知与计算研究中心
Corresponding AuthorRan He
Affiliation1.Center for Research on Intelligent Perception and Computing, CASIA
2.Center for Excellence in Brain Science and Intelligence Technology, CAS
3.University of Chinese Academy of Sciences, Beijing, 100049, China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Lingxiao Song,Zhihe Lu,Ran He,et al. Geometry Guided Adversarial Facial Expression Synthesis[C],2018.
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