  Over the past years, a substantial amount of work has been done on the
problem of facial reenactment, with the solutions coming mainly from the
graphics community. Head reenactment is an even more challenging task, which
aims at transferring not only the facial expression, but also the entire head
pose from a source person to a target. Current approaches either train
person-specific systems, or use facial landmarks to model human heads, a
representation that might transfer unwanted identity attributes from the source
to the target. We propose head2headFS, a novel easily adaptable pipeline for
head reenactment. We condition synthesis of the target person on dense 3D face
shape information from the source, which enables high quality expression and
pose transfer. Our video-based rendering network is fine-tuned under a few-shot
learning strategy, using only a few samples. This allows for fast adaptation of
a generic generator trained on a multiple-person dataset, into a
person-specific one.
