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arXiv · 1805.09488

VisemeNet: Audio-Driven Animator-Centric Speech Animation

Yang Zhou·Zhan Xu·Chris Landreth·Evangelos Kalogerakis·Subhransu Maji·Karan Singh
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Abstract

We present a novel deep-learning based approach to producing animator-centric speech motion curves that drive a JALI or standard FACS-based production face-rig, directly from input audio. Our three-stage Long Short-Term Memory (LSTM) network architecture is motivated by psycho-linguistic insights: segmenting speech audio into a stream of phonetic-groups is sufficient for viseme construction; speech styles like mumbling or shouting are strongly co-related to the motion of facial landmarks; and animator style is encoded in viseme motion curve profiles. Our contribution is an automatic real-time lip-synchronization from audio solution that integrates seamlessly into existing animation pipelines. We evaluate our results by: cross-validation to ground-truth data; animator critique and edits; visual comparison to recent deep-learning lip-synchronization solutions; and showing our approach to be resilient to diversity in speaker and language.

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Yang Zhou, Zhan Xu, Chris Landreth, Evangelos Kalogerakis, Subhransu Maji, Karan Singh. 2018-05-24. VisemeNet: Audio-Driven Animator-Centric Speech Animation. https://arxiv.org/abs/1805.09488

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