  Image reenactment is a task where the target object in the source image
imitates the motion represented in the driving image. One of the most common
reenactment tasks is face image animation. The major challenge in the current
face reenactment approaches is to distinguish between facial motion and
identity. For this reason, the previous models struggle to produce high-quality
animations if the driving and source identities are different (cross-person
reenactment). We propose a new (face) reenactment model that learns
shape-independent motion features in a self-supervised setup. The motion is
represented using a set of paired feature points extracted from the source and
driving images simultaneously. The model is generalised to multiple reenactment
tasks including faces and non-face objects using only a single source image.
The extensive experiments show that the model faithfully transfers the driving
motion to the source while retaining the source identity intact.
