Gaze-annotated facial data is crucial for training deep neural networks
(DNNs) for gaze estimation. However, obtaining these data is labor-intensive
and requires specialized equipment due to the challenge of accurately
annotating the gaze direction of a subject. In this work, we present a
generative framework to create annotated gaze data by leveraging the benefits
of labeled and unlabeled data sources. We propose a Gaze-aware Compositional
GAN that learns to generate annotated facial images from a limited labeled
dataset. Then we transfer this model to an unlabeled data domain to take
advantage of the diversity it provides. Experiments demonstrate our approach's
effectiveness in generating within-domain image augmentations in the ETH-XGaze
dataset and cross-domain augmentations in the CelebAMask-HQ dataset domain for
gaze estimation DNN training. We also show additional applications of our work,
which include facial image editing and gaze redirection.

提出了一种利用有限标记数据集生成注释面部图像的生成框架，并将该模型转移到无标记的数据域，以利用其提供的多样性，进而有效地生成符合眼神估计深度神经网络训练的内部领域图像增强和跨领域增强。