  Several recent works have shown how highly realistic human head images can be
obtained by training convolutional neural networks to generate them. In order
to create a personalized talking head model, these works require training on a
large dataset of images of a single person. However, in many practical
scenarios, such personalized talking head models need to be learned from a few
image views of a person, potentially even a single image. Here, we present a
system with such few-shot capability. It performs lengthy meta-learning on a
large dataset of videos, and after that is able to frame few- and one-shot
learning of neural talking head models of previously unseen people as
adversarial training problems with high capacity generators and discriminators.
Crucially, the system is able to initialize the parameters of both the
generator and the discriminator in a person-specific way, so that training can
be based on just a few images and done quickly, despite the need to tune tens
of millions of parameters. We show that such an approach is able to learn
highly realistic and personalized talking head models of new people and even
portrait paintings.
