DeepPrivacy2: Towards Realistic Full-Body Anonymization. (arXiv:2211.09454v1 [cs.CV])

Generative Adversarial Networks (GANs) are widely adapted for anonymization
of human figures. However, current state-of-the-art limit anonymization to the
task of face anonymization. In this paper, we propose a novel anonymization
framework (DeepPrivacy2) for realistic anonymization of human figures and
faces. We introduce a new large and diverse dataset for human figure synthesis,
which significantly improves image quality and diversity of generated images.
Furthermore, we propose a style-based GAN that produces high quality, diverse
and editable anonymizations. We demonstrate that …

anonymization arxiv