  Animating still face images with deep generative models using a speech input
signal is an active research topic and has seen important recent progress.
However, much of the effort has been put into lip syncing and rendering quality
while the generation of natural head motion, let alone the audio-visual
correlation between head motion and speech, has often been neglected. In this
work, we propose a multi-scale audio-visual synchrony loss and a multi-scale
autoregressive GAN to better handle short and long-term correlation between
speech and the dynamics of the head and lips. In particular, we train a stack
of syncer models on multimodal input pyramids and use these models as guidance
in a multi-scale generator network to produce audio-aligned motion unfolding
over diverse time scales. Our generator operates in the facial landmark domain,
which is a standard low-dimensional head representation. The experiments show
significant improvements over the state of the art in head motion dynamics
quality and in multi-scale audio-visual synchrony both in the landmark domain
and in the image domain.
