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2009
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Speech-Driven Facial Animation Using a Shared Gaussian Process Latent Variable Model

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Speech-Driven Facial Animation Using a Shared Gaussian Process Latent Variable Model
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.
Salil Deena, Aphrodite Galata
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where ISVC
Authors Salil Deena, Aphrodite Galata
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