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{Deng:2019,
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},
year = {2019}
}
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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
PY  - 2019///
TI  - Dense 3D face decoding over 2500FPS: joint texture and shape convolutional mesh decoders
UR  - http://hdl.handle.net/10044/1/69956
ER  -
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