  We propose a neural head reenactment system, which is driven by a latent pose
representation and is capable of predicting the foreground segmentation
alongside the RGB image. The latent pose representation is learned as a part of
the entire reenactment system, and the learning process is based solely on
image reconstruction losses. We show that despite its simplicity, with a large
and diverse enough training dataset, such learning successfully decomposes pose
from identity. The resulting system can then reproduce mimics of the driving
person and, furthermore, can perform cross-person reenactment. Additionally, we
show that the learned descriptors are useful for other pose-related tasks, such
as keypoint prediction and pose-based retrieval.
