Realistic Mouth Animation Based On An Articulatory Dbn Model With Constrained Asynchrony

In this paper, we propose an approach to convert acoustic speech to video realistic mouth animation based on an articulatory dynamic Bayesian network model with constrained asynchrony (AF_AVDBN). Conditional probability distributions are defined to control the asynchronies between the articulators such as lips, tongue and glottis/velum. An EM-based conversion algorithm is also presented to learn the optimal visual features given an auditory input and the trained AF_AVDBN parameters. In the training of the AF_AVDBN models, downsampled YUV spatial frequency features of the interpolated mouth image sequences are extracted as visual features. For reproducing the mouth animation sequence, from the learned visual features, a spatial upsampling and a temporal downsampling are applied. Both qualitative and quantitative results show that the proposed method is capable of producing more natural and realistic mouth animations, and the accuracy is further improved compared to the state of the art multi-stream Hidden Markov Model (MSHMM) and articulatory DBN model without asynchrony constraint (AF_DBN).