Probabilistic Morphable Models (PMMs)

PMMs are a fully open probabilistic framework for a model based image analysis using an analysis by synthesis approach. The framework splits naturally into a component for statistical object modelling and a component for fitting such a model to a novel data. The analysis of novel data is performed by fitting statistical object models to data using MCMC optimization.

Core Publications​

The following publications form the theoretical basis of Probabilistic Morphable Models.

• 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 Schöborn, 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

Applications to face image analysis​

For successful real-world application, carefully set up models are needed. The following papers work out individual steps of the approach in detail for the application of face-image analysis.

• 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.