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PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup

机译:PairedCycleGAN:不对称样式转移,用于涂抹和卸妆

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This paper introduces an automatic method for editing a portrait photo so that the subject appears to be wearing makeup in the style of another person in a reference photo. Our unsupervised learning approach relies on a new framework of cycle-consistent generative adversarial networks. Different from the image domain transfer problem, our style transfer problem involves two asymmetric functions: a forward function encodes example-based style transfer, whereas a backward function removes the style. We construct two coupled networks to implement these functions - one that transfers makeup style and a second that can remove makeup - such that the output of their successive application to an input photo will match the input. The learned style network can then quickly apply an arbitrary makeup style to an arbitrary photo. We demonstrate the effectiveness on a broad range of portraits and styles.
机译:本文介绍了一种自动编辑人像照片的方法,以使对象看起来像是在参考照片中的其他人一样的妆容。我们的无监督学习方法依赖于周期一致的生成对抗网络的新框架。与图像域转移问题不同,我们的样式转移问题涉及两个不对称函数:前向函数编码基于示例的样式转移,而后向函数删除样式。我们构建了两个耦合的网络来实现这些功能-一个传递化妆风格,另一个可以移除化妆-以便将其连续应用程序对输入照片的输出与输入相匹配。然后,学习的样式网络可以快速将任意化妆样式应用于任意照片。我们在各种肖像和风格上证明了其有效性。

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