CUDA-GHR: Controllable Unsupervised Domain Adaptation for Gaze and Head Redirection. (arXiv:2106.10852v4 [cs.CV] UPDATED)

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 …

arxiv cuda domain adaptation head unsupervised