Makeup Like a Superstar: Deep Localized Makeup Transfer Network
Makeup Like a Superstar: Deep Localized Makeup Transfer Network
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发表时间：
2016-04
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通讯作者：
Si Liu;Xinyu Ou;Ruihe Qian;Wei Wang;Xiaochun Cao
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作者：
Si Liu;Xinyu Ou;Ruihe Qian;Wei Wang;Xiaochun Cao
在本文中，我们提出了一种新的深度本地化化妆传输网络，自动推荐最适合女性的化妆和合成她脸上的化妆。给定一张化妆前的脸，她最合适的化妆是自动确定的。然后，化妆前和参考人脸都被馈送到所提出的深度传输网络中以生成化妆后的人脸。我们的端到端化妆品转移网络具有几个很好的特性，包括：（1）功能齐全：包括粉底，唇彩和眼影转移;（2）化妆品专用：不同的化妆品以不同的方式转移;（3）局部化：不同的化妆品涂抹在不同的面部区域;（4）产生自然的外观效果，没有明显的伪影;（5）可控的化妆品亮度：可以产生从淡妆到浓妆的各种结果。定性和定量实验表明，我们的网络比[Guo和Sim，2009]的方法和NerualStyle的两个变体[Gatys等人，2015年a]。
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].