CA-GAN: Weakly Supervised Color Aware GAN for Controllable Makeup Transfer [article]

Robin Kips, Pietro Gori, Matthieu Perrot, Isabelle Bloch
2020 arXiv   pre-print
While existing makeup style transfer models perform an image synthesis whose results cannot be explicitly controlled, the ability to modify makeup color continuously is a desirable property for virtual try-on applications. We propose a new formulation for the makeup style transfer task, with the objective to learn a color controllable makeup style synthesis. We introduce CA-GAN, a generative model that learns to modify the color of specific objects (e.g. lips or eyes) in the image to an
more » ... y target color while preserving background. Since color labels are rare and costly to acquire, our method leverages weakly supervised learning for conditional GANs. This enables to learn a controllable synthesis of complex objects, and only requires a weak proxy of the image attribute that we desire to modify. Finally, we present for the first time a quantitative analysis of makeup style transfer and color control performance.
arXiv:2008.10298v1 fatcat:n4apgkkdlzbgreuemwx3zdosye