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.
@article{arxiv.1805.09488,
title = {VisemeNet: Audio-Driven Animator-Centric Speech Animation},
author = {Yang Zhou and Zhan Xu and Chris Landreth and Evangelos Kalogerakis and Subhransu Maji and Karan Singh},
journal= {arXiv preprint arXiv:1805.09488},
year = {2018}
}