Learning Gaze-aware Compositional GAN
<|reference_start|>Learning Gaze-aware Compositional GAN: 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.<|reference_end|>
@article{aranjuelo2024learning,
    title={Learning Gaze-aware Compositional GAN},
    author={Nerea Aranjuelo, Siyu Huang, Ignacio Arganda-Carreras, Luis Unzueta,
  Oihana Otaegui, Hanspeter Pfister, Donglai Wei},
    journal={Proceedings of the ACM on Computer Graphics and Interactive
  Techniques, 2024},
    year={2024},
    doi={10.1145/3654706},
    archivePrefix={arXiv},
    eprint={2405.20643},
    primaryClass={cs.CV cs.AI}
}