MakeupBag: Disentangling Makeup Extraction and Application
This work addresses the problem of flexible and accurate makeup style transfer for users interested in virtual try-on or digital makeup application, offering customization not possible with existing methods.
This paper introduces MakeupBag, a novel method for automatic makeup style transfer that disentangles makeup extraction and application. It can transfer makeup styles from a reference image to an unseen face, outperforming current state-of-the-art approaches in both classical and extreme makeup transfer.
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.