  The robustness of gaze and head pose estimation models is highly dependent on
the amount of labeled data. Recently, generative modeling has shown excellent
results in generating photo-realistic images, which can alleviate the need for
annotations. However, adopting such generative models to new domains while
maintaining their ability to provide fine-grained control over different image
attributes, \eg, gaze and head pose directions, has been a challenging problem.
This paper proposes CUDA-GHR, an unsupervised domain adaptation framework that
enables fine-grained control over gaze and head pose directions while
preserving the appearance-related factors of the person. Our framework
simultaneously learns to adapt to new domains and disentangle visual attributes
such as appearance, gaze direction, and head orientation by utilizing a
label-rich source domain and an unlabeled target domain. Extensive experiments
on the benchmarking datasets show that the proposed method can outperform
state-of-the-art techniques on both quantitative and qualitative evaluations.
Furthermore, we demonstrate the effectiveness of generated image-label pairs in
the target domain for pretraining networks for the downstream task of gaze and
head pose estimation. The source code and pre-trained models are available at
https://github.com/jswati31/cuda-ghr.
