  Gaze-annotated facial data is crucial for training deep neural networks
(DNNs) for gaze estimation. However, obtaining these data is labor-intensive
and requires specialized equipment due to the challenge of accurately
annotating the gaze direction of a subject. In this work, we present a
generative framework to create annotated gaze data by leveraging the benefits
of labeled and unlabeled data sources. We propose a Gaze-aware Compositional
GAN that learns to generate annotated facial images from a limited labeled
dataset. Then we transfer this model to an unlabeled data domain to take
advantage of the diversity it provides. Experiments demonstrate our approach's
effectiveness in generating within-domain image augmentations in the ETH-XGaze
dataset and cross-domain augmentations in the CelebAMask-HQ dataset domain for
gaze estimation DNN training. We also show additional applications of our work,
which include facial image editing and gaze redirection.
