Abstract
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