  Gaze estimation methods learn eye gaze from facial features. However, among
rich information in the facial image, real gaze-relevant features only
correspond to subtle changes in eye region, while other gaze-irrelevant
features like illumination, personal appearance and even facial expression may
affect the learning in an unexpected way. This is a major reason why existing
methods show significant performance degradation in cross-domain/dataset
evaluation. In this paper, we tackle the domain generalization problem in
cross-domain gaze estimation for unknown target domains. To be specific, we
realize the domain generalization by gaze feature purification. We eliminate
gaze-irrelevant factors such as illumination and identity to improve the
cross-dataset performance without knowing the target dataset. We design a
plug-and-play self-adversarial framework for the gaze feature purification. The
framework enhances not only our baseline but also existing gaze estimation
methods directly and significantly. Our method achieves the state-of-the-art
performance in different benchmarks. Meanwhile, the purification is easily
explainable via visualization.
