  Despite recent advances in appearance-based gaze estimation techniques, the
need for training data that covers the target head pose and gaze distribution
remains a crucial challenge for practical deployment. This work examines a
novel approach for synthesizing gaze estimation training data based on
monocular 3D face reconstruction. Unlike prior works using multi-view
reconstruction, photo-realistic CG models, or generative neural networks, our
approach can manipulate and extend the head pose range of existing training
data without any additional requirements. We introduce a projective matching
procedure to align the reconstructed 3D facial mesh with the camera coordinate
system and synthesize face images with accurate gaze labels. We also propose a
mask-guided gaze estimation model and data augmentation strategies to further
improve the estimation accuracy by taking advantage of synthetic training data.
Experiments using multiple public datasets show that our approach significantly
improves the estimation performance on challenging cross-dataset settings with
non-overlapping gaze distributions.
