Abstract. In this work, synthesis of facial animation is done by modelling the mapping between facial motion and speech using the shared Gaussian process latent variable model. Both data are processed separately and subsequently coupled together to yield a shared latent space. This method allows coarticulation to be modelled by having a dynamical model on the latent space. Synthesis of novel animation is done by first obtaining intermediate latent points from the audio data and then using a Gaussian Process mapping to predict the corresponding visual data. Statistical evaluation of generated visual features against ground truth data compares favourably with known methods of speech animation. The generated videos are found to show proper synchronisation with audio and exhibit correct facial dynamics.
We present a novel approach to speech-driven facial animation using a non-parametric switching state space model based on Gaussian processes. The model is an extension of the shared Gaussian process dynamical model, augmented with switching states. Audio and visual data from a talking head corpus are jointly modelled using the proposed method. The switching states are found using variable length Markov models trained on labelled phonetic data. We also propose a synthesis technique that takes into account both previous and future phonetic context, thus accounting for coarticulatory effects in speech.
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