Makeup like a superstar: Deep Localized Makeup Transfer Network
arXiv:1604.07102
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].
7pages, 11 figures, to appear in IJCAI 2016
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Cited by in corpus (9)
- Fashion Meets Computer Vision: A Survey
- CA-GAN: Weakly Supervised Color Aware GAN for Controllable Makeup Transfer
- PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer
- Content and Colour Distillation for Learning Image Translations with the Spatial Profile Loss
- LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup
- Controllable Descendant Face Synthesis
- PortraitGAN for Flexible Portrait Manipulation
- Local Facial Makeup Transfer via Disentangled Representation
- Digital Makeup from Internet Images