  Along with the recent development of deep neural networks, appearance-based
gaze estimation has succeeded considerably when training and testing within the
same domain. Compared to the within-domain task, the variance of different
domains makes the cross-domain performance drop severely, preventing gaze
estimation deployment in real-world applications. Among all the factors, ranges
of head pose and gaze are believed to play significant roles in the final
performance of gaze estimation, while collecting large ranges of data is
expensive. This work proposes an effective model training pipeline consisting
of a training data synthesis and a gaze estimation model for unsupervised
domain adaptation. The proposed data synthesis leverages the single-image 3D
reconstruction to expand the range of the head poses from the source domain
without requiring a 3D facial shape dataset. To bridge the inevitable gap
between synthetic and real images, we further propose an unsupervised domain
adaptation method suitable for synthetic full-face data. We propose a
disentangling autoencoder network to separate gaze-related features and
introduce background augmentation consistency loss to utilize the
characteristics of the synthetic source domain. Through comprehensive
experiments, it shows that the model using only our synthetic training data can
perform comparably to real data extended with a large label range. Our proposed
domain adaptation approach further improves the performance on multiple target
domains. The code and data will be available at
https://github.com/ut-vision/AdaptiveGaze.
