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Although recent gaze estimation methods lay great emphasis on attentivelyextracting gaze-relevant features from facial or eye images, how to definefeatures that include gaze-relevant components has been ambiguous. Thisobscurity makes the model learn not only gaze-relevant features but alsoirrelevant ones. In particular, it is fatal for the cross-dataset performance.To overcome this challenging issue, we propose a gaze-aware analyticmanipulation method, based on a data-driven approach with generativeadversarial network inversion's disentanglement characteristics, to selectivelyutilize gaze-relevant features in a latent code. Furthermore, by utilizingGAN-based encoder-generator process, we shift the input image from the targetdomain to the source domain image, which a gaze estimator is sufficientlyaware. In addition, we propose gaze distortion loss in the encoder thatprevents the distortion of gaze information. The experimental resultsdemonstrate that our method achieves state-of-the-art gaze estimation accuracyin a cross-domain gaze estimation tasks. This code is available athttps://github.com/leeisack/LatentGaze/.

LatentGaze: Cross-Domain Gaze Estimation through Gaze-Aware Analytic Latent Code Manipulation