We present a system that can learn realistic talking head models. Notably, learning of a new head model requires just a handful of images. In this example, the model was learned from eight frames. Once a talking head model was learned, it can be driven by the positions of the face landmarks. In this case, we extract landmarks by running an off-the-shelf face landmark tracker on a different video of the same person. Effectively, the learned model serves as a realistic avatar of a person. Our talking head models work well, even for new view angles, not present in the training examples as shown here. Our system can learn from different numbers of frames. One shot learning from a single frame is possible. Of course, increasing the number of frames leads to head models of higher realism and to better identity preservation. Our approach uses a meta-learning stage, which is performed on a huge data set of videos. For the results in this presentation, the Vox Celeb 2 data set is used. Three networks are trained during the meta-learning stage. The embedded network maps frames concatenated with the landmarks into vectors, containing pose independent information. These vectors are used to initialize the parameters of adapted layers inside the Generator Network, which maps landmarks into the synthesized video. Finally, the discriminator network assesses the realism, pose, and identity preservation of the generated frames. Data identity preservation is achieved by having a trainable embedding vector inside the discriminator for each training video. For more details, please refer to the paper. After meta-learning, we're able to fine-tune the generator and the discriminator for a new person. The generator and the discriminator networks have tens of millions of parameters. Still, such fine-tuning is possible on just a few images, thanks to the good initialization provided by the meta-learning stage. Before fine-tuning for a new person, we initialize the adapted parameters of the generator and the video embedding inside the discriminator using the output of the embedded network. After that, we train the generator and the discriminator on the available few images using the same adversarial objective as in the meta-learning stage. The adversarial fine-tuning is very important for the improvement of realism and identity matching. Also, it allows us to get a more personalized model, given a larger image set for fine-tuning. The identity matching improvement is particularly noticeable in the bottom example. Here, we show more results for holdout identities on the Vox Celeb 2 data set that were unseen by the system at the meta-learning stage. These talking head models are obtained using eight frames, although in some cases there was limited diversity between the head pose in those eight frames. We also show how the system generalizes to selfie photographs, which are quite different from YouTube video frames in the Vox Celeb 2 data set. Here is one more talking head model, learned on 16 selfie photographs. We can push the generalization even further, applying the system to famous photographs. In each case, we automatically find people in the Vox Celeb 2 data set with landmark suitable for the animation of a particular portrait. With a certain degree of success, we can even apply the models of paintings, despite the large domain gap between paintings and YouTube videos. Here we can see that in some cases, the model might be very sensitive to the geometry of the landmarks. Driving Mona Lisa with landmarks from three different people results in videos with very distinct personalities. That adaptation and tighter integration of our system with landmark tracking remains future work.