  Developing gaze estimation models that generalize well to unseen domains and
in-the-wild conditions remains a challenge with no known best solution. This is
mostly due to the difficulty of acquiring ground truth data that cover the
distribution of faces, head poses, and environments that exist in the real
world. Most recent methods attempt to close the gap between specific source and
target domains using domain adaptation. In this work, we propose to train
general gaze estimation models which can be directly employed in novel
environments without adaptation. To do so, we leverage the observation that
head, body, and hand pose estimation benefit from revising them as dense 3D
coordinate prediction, and similarly express gaze estimation as regression of
dense 3D eye meshes. To close the gap between image domains, we create a
large-scale dataset of diverse faces with gaze pseudo-annotations, which we
extract based on the 3D geometry of the scene, and design a multi-view
supervision framework to balance their effect during training. We test our
method in the task of gaze generalization, in which we demonstrate improvement
of up to 30% compared to state-of-the-art when no ground truth data are
available, and up to 10% when they are. The project material are available for
research purposes at https://github.com/Vagver/3DGazeNet.
