  In this work, we address the task of unconditional head motion generation to
animate still human faces in a low-dimensional semantic space from a single
reference pose. Different from traditional audio-conditioned talking head
generation that seldom puts emphasis on realistic head motions, we devise a
GAN-based architecture that learns to synthesize rich head motion sequences
over long duration while maintaining low error accumulation levels.In
particular, the autoregressive generation of incremental outputs ensures smooth
trajectories, while a multi-scale discriminator on input pairs drives
generation toward better handling of high- and low-frequency signals and less
mode collapse.We experimentally demonstrate the relevance of the proposed
method and show its superiority compared to models that attained
state-of-the-art performances on similar tasks.
