Adaptive Contour Fitting for Pose-Invariant 3D Face Shape Reconstruction

Chengchao Qu, Eduardo Monari, Tobias Schuchert and Jürgen Beyerer

Abstract

Direct reconstruction of 3D face shape—solely based on a sparse set of 2D feature points localized by a facial landmark detector—offers an automatic, efficient and illumination-invariant alternative to the conventional analysis-by-synthesis 3D Morphable Model (3DMM) fitting. In this paper, we propose a novel algorithm that addresses the inconsistent correspondence of 2D and 3D landmarks at the facial contour due to head pose and localization ambiguity along the edge. To facilitate dynamic correspondence while fitting, a small subset of 3D vertices that serves as the contour candidates is annotated offline. During the fitting process, we employ the Levenberg-Marquardt Iterative Closest Point (LM-ICP) algorithm in combination with Distance Transform (DT) within the constrained domain, which allows for fast convergence and robust estimation of 3D face shape against pose variation. Superior evaluation results reported on ground truth 3D face scans over the state-of-the-art demonstrate the efficacy of the proposed method.

Session

3D Vision

Files

Extended Abstract (PDF, 1658K)
Paper (PDF, 5M)

DOI

10.5244/C.29.87
https://dx.doi.org/10.5244/C.29.87

Citation

Bibtex

@inproceedings{BMVC2015_87,
	title={Adaptive Contour Fitting for Pose-Invariant 3D Face Shape Reconstruction},
	author={Chengchao Qu and Eduardo Monari and Tobias Schuchert and Jürgen Beyerer},
	year={2015},
	month={September},
	pages={87.1-87.12},
	articleno={87},
	numpages={12},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.87},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.87}
}