  As the quality of few shot facial animation from landmarks increases, new
applications become possible, such as ultra low bandwidth video chat
compression with a high degree of realism. However, there are some important
challenges to tackle in order to improve the experience in real world
conditions. In particular, the current approaches fail to represent profile
views without distortions, while running in a low compute regime. We focus on
this key problem by introducing a multi-frames embedding dubbed Frontalizer to
improve profile views rendering. In addition to this core improvement, we
explore the learning of a latent code conditioning generations along with
landmarks to better convey facial expressions. Our dense models achieves 22% of
improvement in perceptual quality and 73% reduction of landmark error over the
first order model baseline on a subset of DFDC videos containing head
movements. Declined with mobile architectures, our models outperform the
previous state-of-the-art (improving perceptual quality by more than 16% and
reducing landmark error by more than 47% on two datasets) while running on real
time on iPhone 8 with very low bandwidth requirements.
