Adversarial Learning Semantic Volume for 2D/3D Face Shape Regression in the Wild | |
Zhang, Hongwen1,2; Li, Qi3; Sun, Zhenan1,2 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
ISSN | 1057-7149 |
2019-09-01 | |
卷号 | 28期号:9页码:4526-4540 |
摘要 | 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. |
关键词 | 2D/3D facial landmark localization semantic volumetric representation joint voxel and coordinate regression auxiliary regression adversarial learning |
DOI | 10.1109/TIP.2019.2911114 |
关键词[WOS] | ALIGNMENT |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National 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研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000476797800006 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 生物特征识别 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/26090 |
专题 | 智能感知与计算研究中心 |
通讯作者 | Sun, Zhenan |
作者单位 | 1.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 |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 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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