CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
A Unified Multi-output Semi-supervised Network for 3D Face Reconstruction
Wang, Pengrui1,2; Tian, Yi1,3; Che, Wujun1; Xu, Bo1
2019-07
Conference NameInternational Joint Conference on Neural Networks (IJCNN)
Conference Date2019-07
Conference PlaceBudapest
Abstract

In this paper, we propose a method to reconstruct fine-grained 3D faces from single images base on a nearly unified multi-output regression network. The network estimates the facial shape, normal and appearance jointly in 2D UV map which preserves spatial adjacency relations among vertexes and provides  semantic meaning of each vertex. Three contributions of the proposed method are: 1) we generate the UV map by as-rigid-as-possible parametrization to address the overlapping problem caused by cylindrical unwarp; 2) we directly estimate face normal rather than compute it from the estimated shape to let it catch geometric details from face texture; 3) we propose a post process strategy to generating more realistic faces and to employing the estimated normal. Experiments show that our network is able to learn a uniform appearance and predict more accurate shape from the proposed UV map. Additionally, the post process procedure can improve the quality of facial shapes and add geometric details from estimated normals.

Indexed ByEI
Funding ProjectNational Key RD Plan of China[2017YFB1002804]
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/40396
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Corresponding AuthorChe, Wujun
Affiliation1.Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.Hunan Normal University, China
3.University of Chinese Academy of Sciences, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Pengrui,Tian, Yi,Che, Wujun,et al. A Unified Multi-output Semi-supervised Network for 3D Face Reconstruction[C],2019.
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