Imperial College London

STEFANOS ZAFEIRIOU, PhD

Faculty of EngineeringDepartment of Computing

Reader in Machine Learning and 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{Deng,
author = {Deng, J and Zhou, Y and Kotsia, I and Zafeiriou, S},
publisher = {IEEE},
title = {Dense 3D face decoding over 2500FPS: joint texture and shape convolutional mesh decoders},
url = {http://hdl.handle.net/10044/1/69956},
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - 3D Morphable Models (3DMMs) are statistical modelsthat represent facial texture and shape variations using a setof linear bases and more particular Principal ComponentAnalysis (PCA). 3DMMs were used as statistical priors forreconstructing 3D faces from images by solving non-linearleast square optimization problems. Recently, 3DMMs wereused as generative models for training non-linear mappings(i.e., regressors) from image to the parameters of the modelsvia Deep Convolutional Neural Networks (DCNNs). Nev-ertheless, all of the above methods use either fully con-nected layers or 2D convolutions on parametric unwrappedUV spaces leading to large networks with many parame-ters. In this paper, we present the first, to the best of ourknowledge, non-linear 3DMMs by learning joint textureand shape auto-encoders using direct mesh convolutions.We demonstrate how these auto-encoders can be used totrain very light-weight models that perform Coloured MeshDecoding (CMD) in-the-wild at a speed of over 2500 FPS.
AU - Deng,J
AU - Zhou,Y
AU - Kotsia,I
AU - Zafeiriou,S
PB - IEEE
TI - Dense 3D face decoding over 2500FPS: joint texture and shape convolutional mesh decoders
UR - http://hdl.handle.net/10044/1/69956
ER -