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 Cascaded Framework For Model-Based 3D Face Reconstruction
Wang, Pengrui1,2; Che, Wujun1; Xu, Bo1
2018-04
Conference NameInternational Conference on Acoustics, Speech and Signal Processing (ICASSP)
Conference Date2018-04
Conference PlaceCalgary, Canada
Abstract

This paper presents a general framework for model-based 3D face reconstruction from a single image, which can incorporate mature face alignment methods and utilize their properties. In the proposed framework, the final model parameters, i.e., mostly including pose, identity and expression, are achieved by estimating updating the face landmarks and 3D face model parameter alternately. In addition, we propose the parameter augmented regression method (PARM) as an novel derivation of the framework. Compared with existing methods, PARM is able to utilize mature face alignment methods and use fairly simple features in addition to image appearances for the reconstruction task. Experiments on three derivation methods of the framework show that the proposed framework is feasible and PARM is quite an effective and fast method. With face alignment method LBF, PARM can run over 90 fps on a desktop.

KeywordModel-based 3D face reconstruction face alignment cascaded regression supervised descent method
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/40395
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
2.University of Chinese Academy of Sciences
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,Che, Wujun,Xu, Bo. A Cascaded Framework For Model-Based 3D Face Reconstruction[C],2018.
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