Makeup like a superstar: Deep Localized Makeup Transfer Network
This addresses the need for automated and personalized makeup application in beauty and entertainment domains, but it is incremental as it builds on prior makeup transfer and neural style techniques.
The paper tackles the problem of automatically recommending and synthesizing suitable makeup on a female's face by proposing a Deep Localized Makeup Transfer Network, which outperforms existing methods in qualitative and quantitative experiments.
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].