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A CASCADED FRAMEWORK FOR MODEL-BASED 3D FACE RECONSTRUCTION

Citation Author(s):
Pengrui Wang, Wujun Che, Bo Xu
Submitted by:
Pengrui Wang
Last updated:
20 April 2018 - 2:51am
Document Type:
Poster
Document Year:
2018
Event:
Presenters Name:
Pengrui Wang
Paper Code:
4174

Abstract 

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.

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Dataset Files

Poster_PARM_wang.pdf

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