CASIA OpenIR  > 智能感知与计算
Adversarial Learning Semantic Volume for 2D/3D Face Shape Regression in the Wild
Zhang, Hongwen1,2; Li, Qi3; Sun, Zhenan1,2
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
2019-09-01
Volume28Issue:9Pages:4526-4540
Abstract

Regression-based methods have revolutionized 2D landmark localization with the exploitation of deep neural networks and massive annotated datasets in the wild. However, it remains challenging for 3D landmark localization due to the lack of annotated datasets and the ambiguous nature of landmarks under the 3D perspective. This paper revisits regressionbased methods and proposes an adversarial voxel and coordinate regression framework for 2D and 3D facial landmark localization in real-world scenarios. First, a semantic volumetric representation is introduced to encode the per-voxel likelihood of positions being the 3D landmarks. Then, an end-to-end pipeline is designed to jointly regress the proposed volumetric representation and the coordinate vector. Such a pipeline not only enhances the robustness and accuracy of the predictions but also unifies the 2D and 3D landmark localization so that the 2D and 3D datasets could be utilized simultaneously. Further, an adversarial learning strategy is exploited to distill 3D structure learned from synthetic datasets to real-world datasets under weakly supervised settings, where an auxiliary regression discriminator is proposed to encourage the network to produce plausible predictions for both the synthetic and real-world images. The effectiveness of our method is validated on benchmark datasets 3DFAW and AFLW2000-3D for both 2D and 3D facial landmark localization tasks. The experimental results show that the proposed method achieves significant improvements over the previous state-of-the-art methods.

Keyword2D/3D facial landmark localization semantic volumetric representation joint voxel and coordinate regression auxiliary regression adversarial learning
DOI10.1109/TIP.2019.2911114
WOS KeywordALIGNMENT
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61806197] ; National Natural Science Foundation of China[61702513] ; National Key Research and Development Program of China[2017YFC0821602] ; National Natural Science Foundation of China[61573360] ; National Natural Science Foundation of China[61427811] ; National Natural Science Foundation of China[U1836217] ; National Natural Science Foundation of China[61721004] ; National Key Research and Development Program of China[2016YFB1001000] ; National Key Research and Development Program of China[2016YFB1001000] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[U1836217] ; National Natural Science Foundation of China[61427811] ; National Natural Science Foundation of China[61573360] ; National Key Research and Development Program of China[2017YFC0821602] ; National Natural Science Foundation of China[61702513] ; National Natural Science Foundation of China[61806197]
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000476797800006
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26090
Collection智能感知与计算
Corresponding AuthorSun, Zhenan
Affiliation1.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit,Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
3.Chinese Acad Sci, Ctr Res Intelligent Percept & Com, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R 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
Zhang, Hongwen,Li, Qi,Sun, Zhenan. Adversarial Learning Semantic Volume for 2D/3D Face Shape Regression in the Wild[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2019,28(9):4526-4540.
APA Zhang, Hongwen,Li, Qi,&Sun, Zhenan.(2019).Adversarial Learning Semantic Volume for 2D/3D Face Shape Regression in the Wild.IEEE TRANSACTIONS ON IMAGE PROCESSING,28(9),4526-4540.
MLA Zhang, Hongwen,et al."Adversarial Learning Semantic Volume for 2D/3D Face Shape Regression in the Wild".IEEE TRANSACTIONS ON IMAGE PROCESSING 28.9(2019):4526-4540.
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