论文标题
CA-GAN：弱监督的颜色意识到的gan，可控制化妆转移
CA-GAN: Weakly Supervised Color Aware GAN for Controllable Makeup Transfer
论文作者
论文摘要
尽管现有的化妆样式转移模型执行了无法明确控制结果的图像合成，但连续修改化妆颜色的能力是虚拟试用应用程序的理想属性。我们为化妆样式转移任务提供了一种新的配方，目的是学习颜色可控化妆样式的合成。我们介绍了Ca-Gan，这是一种生成模型，该模型学会了将图像中特定对象的颜色（例如嘴唇或眼睛）修改为任意目标颜色的颜色，同时保留背景。由于颜色标签很少见，而且获取成本很高，因此我们的方法利用了有条件的gan的弱监督学习。这使得能够学习复杂对象的可控综合，并且只需要我们想要修改的图像属性的弱代理。最后，我们首次对化妆样式转移和颜色控制性能进行定量分析。
While existing makeup style transfer models perform an image synthesis whose results cannot be explicitly controlled, the ability to modify makeup color continuously is a desirable property for virtual try-on applications. We propose a new formulation for the makeup style transfer task, with the objective to learn a color controllable makeup style synthesis. We introduce CA-GAN, a generative model that learns to modify the color of specific objects (e.g. lips or eyes) in the image to an arbitrary target color while preserving background. Since color labels are rare and costly to acquire, our method leverages weakly supervised learning for conditional GANs. This enables to learn a controllable synthesis of complex objects, and only requires a weak proxy of the image attribute that we desire to modify. Finally, we present for the first time a quantitative analysis of makeup style transfer and color control performance.
