  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 cross-domain problem in gaze
estimation. Different from common domain adaption methods, we propose a domain
generalization method to improve the cross-domain performance without touching
target samples. The domain generalization is realized by gaze feature
purification. We eliminate gaze-irrelevant factors such as illumination and
identity to improve the cross-domain performance. 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. To the best of our knowledge, we are the first to
propose domain generalization methods in gaze estimation. Our method achieves
not only state-of-the-art performance among typical gaze estimation methods but
also competitive results among domain adaption methods. The code is released in
https://github.com/yihuacheng/PureGaze.
