Makeup Like a Superstar: Deep Localized Makeup Transfer Network
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]
IJCAI