论文标题
Makeupbag：解开化妆提取和应用
MakeupBag: Disentangling Makeup Extraction and Application
论文作者
论文摘要
本文介绍了Makeupbag，这是一种自动化妆样式转移的新方法。我们提出的技术可以将新的化妆样式从参考面图像转移到另一个以前看不见的面部照片。与其他当前纠缠这两个任务的深层方法相比，我们将化妆脱牙和面部化妆应用作为可分离的目标。 Makeupbag为我们的方法提供了重要的优势，因为它允许对提取的化妆样式进行自定义和像素的特定修改，这是使用当前方法的可能性。进行了广泛的实验，包括定性和数值，证明了我们方法产生的图像的高质量和准确性。此外，与大多数当前的其他方法相比，Makeupbag都涉及古典和极端和服装的化妆转移。在比较分析中，Makeupbag显示出优于当前最新方法。
This paper introduces MakeupBag, a novel method for automatic makeup style transfer. Our proposed technique can transfer a new makeup style from a reference face image to another previously unseen facial photograph. We solve makeup disentanglement and facial makeup application as separable objectives, in contrast to other current deep methods that entangle the two tasks. MakeupBag presents a significant advantage for our approach as it allows customization and pixel specific modification of the extracted makeup style, which is not possible using current methods. Extensive experiments, both qualitative and numerical, are conducted demonstrating the high quality and accuracy of the images produced by our method. Furthermore, in contrast to most other current methods, MakeupBag tackles both classical and extreme and costume makeup transfer. In a comparative analysis, MakeupBag is shown to outperform current state-of-the-art approaches.
