  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].
