Predictive sampling of facial expression dynamics driven by a latent action space

We present a probabilistic generative model for tracking by prediction the dynamics of affective spacial expressions in videos. The model relies on Bayesian filter sampling of facial landmarks conditioned on motor action parameter dynamics; namely, trajectories shaped by an autoregressive Gaussian Process Latent Variable state-space. The analysis-by synthesis approach at the heart of the model allows for both inference and generation of affective expressions. Robustness of the method to occlusions and degradation of video quality has been assessed on a publicly available dataset.