Imperial College London
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STEFANOS ZAFEIRIOU, PhD

Faculty of Engineering, Department of Computing

Professor in Machine Learning & Computer Vision
 
 
 
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Contact

 

+44 (0)20 7594 8461s.zafeiriou Website CV

 
 
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Location

 

375Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Guler:2017:10.1109/CVPR.2017.280,
author = {Guler, RA and Trigeorgis, G and Antonakos, E and Snape, P and Zafeiriou, S and Kokkinos, I},
doi = {10.1109/CVPR.2017.280},
pages = {2614--2623},
publisher = {IEEE},
title = {DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild},
url = {http://dx.doi.org/10.1109/CVPR.2017.280},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - In this paper we propose to learn a mapping from image pixels into a dense template grid through a fully convolutional network. We formulate this task as a regression problem and train our network by leveraging upon manually annotated facial landmarks "in-the-wild". We use such landmarks to establish a dense correspondence field between a three-dimensional object template and the input image, which then serves as the ground-truth for training our regression system. We show that we can combine ideas from semantic segmentation with regression networks, yielding a highly-accurate quantized regression architecture. Our system, called DenseReg, allows us to estimate dense image-to-template correspondences in a fully convolutional manner. As such our network can provide useful correspondence information as a stand-alone system, while when used as an initialization for Statistical Deformable Models we obtain landmark localization results that largely outperform the current state-of-the-art on the challenging 300W benchmark. We thoroughly evaluate our method on a host of facial analysis tasks, and demonstrate its use for other correspondence estimation tasks, such as the human body and the human ear. DenseReg code is made available at http://alpguler.com/DenseReg.html along with supplementary materials.
AU  - Guler,RA
AU  - Trigeorgis,G
AU  - Antonakos,E
AU  - Snape,P
AU  - Zafeiriou,S
AU  - Kokkinos,I
DO  - 10.1109/CVPR.2017.280
EP  - 2623
PB  - IEEE
PY  - 2017///
SN  - 1063-6919
SP  - 2614
TI  - DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild
UR  - http://dx.doi.org/10.1109/CVPR.2017.280
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000418371402072&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/60883
ER  -
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