  For realistic talking head generation, creating natural head motion while
maintaining accurate lip synchronization is essential. To fulfill this
challenging task, we propose DisCoHead, a novel method to disentangle and
control head pose and facial expressions without supervision. DisCoHead uses a
single geometric transformation as a bottleneck to isolate and extract head
motion from a head-driving video. Either an affine or a thin-plate spline
transformation can be used and both work well as geometric bottlenecks. We
enhance the efficiency of DisCoHead by integrating a dense motion estimator and
the encoder of a generator which are originally separate modules. Taking a step
further, we also propose a neural mix approach where dense motion is estimated
and applied implicitly by the encoder. After applying the disentangled head
motion to a source identity, DisCoHead controls the mouth region according to
speech audio, and it blinks eyes and moves eyebrows following a separate
driving video of the eye region, via the weight modulation of convolutional
neural networks. The experiments using multiple datasets show that DisCoHead
successfully generates realistic audio-and-video-driven talking heads and
outperforms state-of-the-art methods. Project page:
https://deepbrainai-research.github.io/discohead/
