Makeup studies have recently caught much attention in computer version. Two of the typical tasks are makeup-invariant face verification and makeup transfer. Although having experienced remarkable progress, both tasks remain challenging, especially encountering data in the wild. In this paper, we propose a disentangled feature learning strategy to fulfil both tasks in a single generative network. Overall, a makeup portrait can be decomposed into three components: makeup, identity and geometry (including expression, pose etc.). We assume that the extracted image representation can be decomposed into a makeup code that captures the makeup style and an identity code to preserve the source identity. As for other variation factors, we consider them as native structures from the source image that should be reserved. Thus a dense correspondence field is integrated in the network to preserve the geometry on a face. To encourage delightful visual results after makeup transfer, we propose a cosmetic loss to learn makeup styles in a delicate way. Finally, a new Cross-Makeup Face (CMF) benchmark dataset (https://github.com/ly-joy/Cross-Makeup-Face) with in-the-wild makeup portraits is built up to push the frontiers of related research. Both visual and quantitative experimental results on four makeup datasets demonstrate the superiority of the proposed method.

中文翻译：

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野外化妆肖像的解缠结表示学习

化妆研究最近在电脑版中备受关注。两个典型的任务是妆容不变的面部验证和妆容转移。尽管取得了显着进展，但这两项任务仍然具有挑战性，尤其是在野外遇到数据。在本文中，我们提出了一种分离的特征学习策略，以在单个生成网络中完成这两项任务。总的来说，一张妆容可以分解为三个部分：妆容、身份和几何（包括表情、姿势等）。我们假设提取的图像表示可以分解为捕获化妆风格的化妆代码和保存源身份的身份代码。至于其他变异因素，我们认为它们是应该保留的源图像的原生结构。因此，在网络中集成了一个密集的对应域，以保留面部的几何形状。为了在化妆后获得令人愉悦的视觉效果，我们提出了化妆损失，以微妙的方式学习化妆风格。最后，建立了一个新的跨化妆人脸（CMF）基准数据集（https://github.com/ly-joy/Cross-Makeup-Face），其中包含野外化妆肖像，以推动相关研究的前沿. 四个化妆数据集的视觉和定量实验结果都证明了所提出方法的优越性。com/ly-joy/Cross-Makeup-Face) 与野外化妆肖像建立起来，以推动相关研究的前沿。四个化妆数据集的视觉和定量实验结果都证明了所提出方法的优越性。com/ly-joy/Cross-Makeup-Face) 与野外化妆肖像建立起来，以推动相关研究的前沿。四个化妆数据集的视觉和定量实验结果都证明了所提出方法的优越性。