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We present a deep learning framework for real-time speech-driven 3D facialanimation from just raw waveforms. Our deep neural network directly maps aninput sequence of speech audio to a series of micro facial action unitactivations and head rotations to drive a 3D blendshape face model. Inparticular, our deep model is able to learn the latent representations oftime-varying contextual information and affective states within the speech.Hence, our model not only activates appropriate facial action units atinference to depict different utterance generating actions, in the form of lipmovements, but also, without any assumption, automatically estimates emotionalintensity of the speaker and reproduces her ever-changing affective states byadjusting strength of facial unit activations. For example, in a happy speech,the mouth opens wider than normal, while other facial units are relaxed; or ina surprised state, both eyebrows raise higher. Experiments on a diverseaudiovisual corpus of different actors across a wide range of emotional statesshow interesting and promising results of our approach. Beingspeaker-independent, our generalized model is readily applicable to varioustasks in human-machine interaction and animation.

End-to-end Learning for 3D Facial Animation from Raw Waveforms of Speech