  We describe our novel deep learning approach for driving animated faces using
both acoustic and visual information. In particular, speech-related facial
movements are generated using audiovisual information, and non-speech facial
movements are generated using only visual information. To ensure that our model
exploits both modalities during training, batches are generated that contain
audio-only, video-only, and audiovisual input features. The probability of
dropping a modality allows control over the degree to which the model exploits
audio and visual information during training. Our trained model runs in
real-time on resource limited hardware (e.g.\ a smart phone), it is user
agnostic, and it is not dependent on a potentially error-prone transcription of
the speech. We use subjective testing to demonstrate: 1) the improvement of
audiovisual-driven animation over the equivalent video-only approach, and 2)
the improvement in the animation of speech-related facial movements after
introducing modality dropout. Before introducing dropout, viewers prefer
audiovisual-driven animation in 51% of the test sequences compared with only
18% for video-driven. After introducing dropout viewer preference for
audiovisual-driven animation increases to 74%, but decreases to 8% for
video-only.
