  Speech-driven 3D facial animation has been widely studied, yet there is still
a gap to achieving realism and vividness due to the highly ill-posed nature and
scarcity of audio-visual data. Existing works typically formulate the
cross-modal mapping into a regression task, which suffers from the
regression-to-mean problem leading to over-smoothed facial motions. In this
paper, we propose to cast speech-driven facial animation as a code query task
in a finite proxy space of the learned codebook, which effectively promotes the
vividness of the generated motions by reducing the cross-modal mapping
uncertainty. The codebook is learned by self-reconstruction over real facial
motions and thus embedded with realistic facial motion priors. Over the
discrete motion space, a temporal autoregressive model is employed to
sequentially synthesize facial motions from the input speech signal, which
guarantees lip-sync as well as plausible facial expressions. We demonstrate
that our approach outperforms current state-of-the-art methods both
qualitatively and quantitatively. Also, a user study further justifies our
superiority in perceptual quality.
