Speaker-Independent Speech Animation Using Perceptual Loss Functions and Synthetic Data

We propose a real-time speaker-independent speech-to-facial animation system that predicts lip and jaw movements on a reference face for audio speech taken from any speaker. Our approach is motivated by two key observations; 1) Speakerindependent facial animation can be generated from phoneme labels, but to perform this automatically a speech recogniser is needed which, due to contextual look-ahead, introduces too much time lag. 2) Audio-driven speech animation can be performed in real-time but requires large, multi-speaker audio-visual speech datasets of which there are few. We adopt a novel threestage training procedure that leverages the advantages of each approach. First we train a
phoneme
-to-visual speech model from a large single-speaker audio-visual dataset. Next, we use this model to generate the synthetic visual component of a large multi-speaker audio dataset of which the video is not available. Finally, we learn an
audio
-to-visual speech mapping using the synthetic visual features as the target. Furthermore, we increase the realism of the predicted facial animation by introducing two perceptually-based loss functions that aim to improve mouth closures and openings. The proposed method and loss functions are evaluated objectively using mean square error, global variance and a new metric that measures the extent of mouth opening. Subjective tests show that our approach produces facial animation comparable to those produced from phoneme sequences and that improved mouth closures, particularly for bilabial closures, are achieved.