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We propose an end to end deep learning approach for generating real-timefacial animation from just audio. Specifically, our deep architecture employsdeep bidirectional long short-term memory network and attention mechanism todiscover the latent representations of time-varying contextual informationwithin the speech and recognize the significance of different informationcontributed to certain face status. Therefore, our model is able to drivedifferent levels of facial movements at inference and automatically keep upwith the corresponding pitch and latent speaking style in the input audio, withno assumption or further human intervention. Evaluation results show that ourmethod could not only generate accurate lip movements from audio, but alsosuccessfully regress the speaker's time-varying facial movements.

Audio2Face: Generating Speech/Face Animation from Single Audio with Attention-Based Bidirectional LSTM Networks