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Erschienen in:
Real-Time Semantic Segmentation with Label Propagation Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Shape Augmented Regression for 3D Face Alignment

verfasst von : Chao Gou, Yue Wu, Fei-Yue Wang, Qiang Ji

Erschienen in: Computer Vision – ECCV 2016 Workshops

Verlag: Springer International Publishing

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Abstract

2D face alignment has been an active topic and is becoming mature for real applications. However, when large head pose exists, 2D annotated points lose geometric correspondence with respect to actual 3D location. In addition, local appearance varies more dramatically when subjects are with large pose or under various illuminations. 3D face alignment from 2D images is a promising solution to tackle this problem. 3D face alignment aims to estimate the 3D face shape which is consistent across all poses. In this paper, we propose a novel 3D face alignment method. This method consists of two steps. First, we perform 2D landmark detection based on the shape augmented regression. Second, we estimate the 3D shape using the detected 2D landmarks and 3D deformable model. Experimental results on benchmark database demonstrate its preferable performances.

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Vorheriges Kapitel Fast and Precise Face Alignment and 3D Shape Reconstruction from a Single 2D Image
Nächstes Kapitel Two-Stage Convolutional Part Heatmap Regression for the 1st 3D Face Alignment in the Wild (3DFAW) Challenge
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Metadaten
Titel
Shape Augmented Regression for 3D Face Alignment
verfasst von
Chao Gou
Yue Wu
Fei-Yue Wang
Qiang Ji
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016 Workshops

Print ISBN: 978-3-319-48880-6

Electronic ISBN: 978-3-319-48881-3

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-48881-3_42