Makeup like a superstar: Deep Localized Makeup Transfer Network release_xar2z55tt5gepofv72yvvjtvpa

by Si Liu, Xinyu Ou, Ruihe Qian, Wei Wang, Xiaochun Cao

Released as a article .

2016  

Abstract

In this paper, we propose a novel Deep Localized Makeup Transfer Network to automatically recommend the most suitable makeup for a female and synthesis the makeup on her face. Given a before-makeup face, her most suitable makeup is determined automatically. Then, both the beforemakeup and the reference faces are fed into the proposed Deep Transfer Network to generate the after-makeup face. Our end-to-end makeup transfer network have several nice properties including: (1) with complete functions: including foundation, lip gloss, and eye shadow transfer; (2) cosmetic specific: different cosmetics are transferred in different manners; (3) localized: different cosmetics are applied on different facial regions; (4) producing naturally looking results without obvious artifacts; (5) controllable makeup lightness: various results from light makeup to heavy makeup can be generated. Qualitative and quantitative experiments show that our network performs much better than the methods of [Guo and Sim, 2009] and two variants of NerualStyle [Gatys et al., 2015a].
In text/plain format

Archived Files and Locations

application/pdf  1.5 MB
file_lhe3lbz4vfbtlkebjdvmwobkm4
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2016-04-25
Version   v1
Language   en ?
arXiv  1604.07102v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 341493c7-0b40-42b2-84fd-c9743e683647
API URL: JSON