Beholder-GAN: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level

Nir Diamant, Dean Zadok, Chaim Baskin, Eli Schwartz, Alex M. Bronstein
2/7/2019
cs.CV

Official code: beholdergan/Beholder-GAN (95 stars, last commit 2021-01-27).

Abstract

Beauty is in the eye of the beholder. This maxim, emphasizing the subjectivity of the perception of beauty, has enjoyed a wide consensus since ancient times. In the digitalera, data-driven methods have been shown to be able to predict human-assigned beauty scores for facial images. In this work, we augment this ability and train a generative model that generates faces conditioned on a requested beauty score. In addition, we show how this trained generator can be used to beautify an input face image. By doing so, we achieve an unsupervised beautification model, in the sense that it relies on no ground truth target images.

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Generation and Beautification of Facial Images with Conditioning on Their Beauty Level

9525PythonFeb 7, 20195 years ago

Cite this paper

@article{diamant2019beholdergan,
  title  = {Beholder-GAN: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level},
  author = {Nir Diamant and Dean Zadok and Chaim Baskin and Eli Schwartz and Alex M. Bronstein},
  year   = {2019},
  eprint = {1902.02593},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1902.02593}
}

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