{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/beholder-gan-generation-and-beautification-of","title":"Beholder-GAN: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level","arxiv_id":"1902.02593","date":"2019-02-07","proceeding":null,"authors":["Nir Diamant","Dean Zadok","Chaim Baskin","Eli Schwartz","Alex M. Bronstein"],"abstract":"Beauty is in the eye of the beholder. This maxim, emphasizing the\nsubjectivity of the perception of beauty, has enjoyed a wide consensus since\nancient times. In the digitalera, data-driven methods have been shown to be\nable to predict human-assigned beauty scores for facial images. In this work,\nwe augment this ability and train a generative model that generates faces\nconditioned on a requested beauty score. In addition, we show how this trained\ngenerator can be used to beautify an input face image. By doing so, we achieve\nan unsupervised beautification model, in the sense that it relies on no ground\ntruth target images.","url_abs":"http://arxiv.org/abs/1902.02593v3","url_pdf":"http://arxiv.org/pdf/1902.02593v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"beholder-gan-generation-and-beautification-of","repo_url":"https://github.com/beholdergan/Beholder-GAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"beholder-gan-generation-and-beautification-of","repo_url":"https://github.com/deanzadok/Beholder-GAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}