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Towards Disentangling Latent Space for Unsupervised Semantic Face Editing
[article]
2021
arXiv
pre-print
Facial attributes in StyleGAN generated images are entangled in the latent space which makes it very difficult to independently control a specific attribute without affecting the others. Supervised attribute editing requires annotated training data which is difficult to obtain and limits the editable attributes to those with labels. Therefore, unsupervised attribute editing in an disentangled latent space is key to performing neat and versatile semantic face editing. In this paper, we present a
arXiv:2011.02638v2
fatcat:ygf5pkylbvdrxcxytekgxv452i