  We present Free-HeadGAN, a person-generic neural talking head synthesis
system. We show that modeling faces with sparse 3D facial landmarks are
sufficient for achieving state-of-the-art generative performance, without
relying on strong statistical priors of the face, such as 3D Morphable Models.
Apart from 3D pose and facial expressions, our method is capable of fully
transferring the eye gaze, from a driving actor to a source identity. Our
complete pipeline consists of three components: a canonical 3D key-point
estimator that regresses 3D pose and expression-related deformations, a gaze
estimation network and a generator that is built upon the architecture of
HeadGAN. We further experiment with an extension of our generator to
accommodate few-shot learning using an attention mechanism, in case more than
one source images are available. Compared to the latest models for reenactment
and motion transfer, our system achieves higher photo-realism combined with
superior identity preservation, while offering explicit gaze control.
