  We propose a local adversarial disentangling network (LADN) for facial makeup
and de-makeup. Central to our method are multiple and overlapping local
adversarial discriminators in a content-style disentangling network for
achieving local detail transfer between facial images, with the use of
asymmetric loss functions for dramatic makeup styles with high-frequency
details. Existing techniques do not demonstrate or fail to transfer
high-frequency details in a global adversarial setting, or train a single local
discriminator only to ensure image structure consistency and thus work only for
relatively simple styles. Unlike others, our proposed local adversarial
discriminators can distinguish whether the generated local image details are
consistent with the corresponding regions in the given reference image in
cross-image style transfer in an unsupervised setting. Incorporating these
technical contributions, we achieve not only state-of-the-art results on
conventional styles but also novel results involving complex and dramatic
styles with high-frequency details covering large areas across multiple facial
features. A carefully designed dataset of unpaired before and after makeup
images is released.
