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To be widely adopted, 3D facial avatars must be animated easily,realistically, and directly from speech signals. While the best recent methodsgenerate 3D animations that are synchronized with the input audio, they largelyignore the impact of emotions on facial expressions. Realistic facial animationrequires lip-sync together with the natural expression of emotion. To that end,we propose EMOTE (Expressive Model Optimized for Talking with Emotion), whichgenerates 3D talking-head avatars that maintain lip-sync from speech whileenabling explicit control over the expression of emotion. To achieve this, wesupervise EMOTE with decoupled losses for speech (i.e., lip-sync) and emotion.These losses are based on two key observations: (1) deformations of the facedue to speech are spatially localized around the mouth and have high temporalfrequency, whereas (2) facial expressions may deform the whole face and occurover longer intervals. Thus, we train EMOTE with a per-frame lip-reading lossto preserve the speech-dependent content, while supervising emotion at thesequence level. Furthermore, we employ a content-emotion exchange mechanism inorder to supervise different emotions on the same audio, while maintaining thelip motion synchronized with the speech. To employ deep perceptual losseswithout getting undesirable artifacts, we devise a motion prior in the form ofa temporal VAE. Due to the absence of high-quality aligned emotional 3D facedatasets with speech, EMOTE is trained with 3D pseudo-ground-truth extractedfrom an emotional video dataset (i.e., MEAD). Extensive qualitative andperceptual evaluations demonstrate that EMOTE produces speech-driven facialanimations with better lip-sync than state-of-the-art methods trained on thesame data, while offering additional, high-quality emotional control.

Emotional Speech-Driven Animation with Content-Emotion Disentanglement