CA-GAN: Weakly Supervised Color Aware GAN for Controllable Makeup Transfer
This addresses virtual try-on applications by enabling explicit color control, though it is incremental as it builds on existing GAN-based makeup transfer methods.
The paper tackles the problem of controllable makeup color transfer in images, proposing CA-GAN to modify specific objects like lips or eyes to arbitrary target colors while preserving the background, achieving results with quantitative analysis showing improved performance.
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 arbitrary 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.