Literature

The scientific basis of our PMM methodology is the combination of Gaussian Processes for modelling the variability of object classes and a stochastic optimization framework bases on a Markov Chain Monte Carlo method.

The analysis of novel data is performed by fitting the GP to the novel data using MCMC optimization.

Publications

• Gaussian Process Morphable Models [pdf]
  Marcel Luethi, Thomas Gerig, Christoph Jud and Thomas Vetter
  IEEE Transactions on Pattern Analysis and Machine Intelligence (Volume: 40 , Issue: 8 , Aug. 1 2018)
  a preprint can be found on ArXiv: http://arxiv.org/format/1603.07254v1

• Markov Chain Monte Carlo for Automated Face Image Analysis [pdf]
  Sandro Schönborn, Bernhard Egger, Andreas Morel-Forster and Thomas Vetter
  International Journal of Computer Vision 123(2), 160-183 , June 2017
  DOI: http://dx.doi.org/10.1007/s11263-016-0967-5

advanced topics

• Occlusion-aware 3D Morphable Models and an Illumination Prior for Face Image Analysis [pdf]
  Bernhard Egger, Sandro Schoenborn, Andreas Schneider, Adam Kortylewski, Andreas Morel-Forster, Clemens Blumer and Thomas Vetter
  International Journal of Computer Vision (IJCV), 2018
  DOI: https://doi.org/10.1007/s11263-018-1064-8

• Efficient Global Illumination for Morphable Models [pdf]
  Andreas Schneider, Sandro Schonborn, Lavrenti Frobeen, Bernhard Egger, Thomas Vetter;
  The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 3865-3873

• Morphable Face Models - An Open Framework [pdf]
  Thomas Gerig, Andreas Morel-Forster, Clemens Blumer, Bernhard Egger, Marcel Lüthi, Sandro Schönborn and Thomas Vetter
  13th IEEE Conference on Automatic Face and Gesture Recognition (FG 2018). pp. 75-82.