A Cascaded Framework for Model-Based 3D Face Reconstruction

Pengrui Wang; Wujun Che; Bo Xu

ICASSP 2018accepted0 citations

A Cascaded Framework for Model-Based 3D Face Reconstruction

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.

BibTeX

@inproceedings{icassp2018_acascadedframewo,
  title = {A Cascaded Framework for Model-Based 3D Face Reconstruction},
  author = {Pengrui Wang and Wujun Che and Bo Xu},
  booktitle = {ICASSP 2018},
  year = {2018}
}