  Although recent gaze estimation methods lay great emphasis on attentively
extracting gaze-relevant features from facial or eye images, how to define
features that include gaze-relevant components has been ambiguous. This
obscurity makes the model learn not only gaze-relevant features but also
irrelevant ones. In particular, it is fatal for the cross-dataset performance.
To overcome this challenging issue, we propose a gaze-aware analytic
manipulation method, based on a data-driven approach with generative
adversarial network inversion's disentanglement characteristics, to selectively
utilize gaze-relevant features in a latent code. Furthermore, by utilizing
GAN-based encoder-generator process, we shift the input image from the target
domain to the source domain image, which a gaze estimator is sufficiently
aware. In addition, we propose gaze distortion loss in the encoder that
prevents the distortion of gaze information. The experimental results
demonstrate that our method achieves state-of-the-art gaze estimation accuracy
in a cross-domain gaze estimation tasks. This code is available at
https://github.com/leeisack/LatentGaze/.
