A multi-granularity facial extreme makeup transfer and removal model with local-global collaboration

Most existing face makeup transfer methods have limitations in accuracy, realism, and identity preservation. These methods cannot transfer extreme makeup with complex patterns, only light ones. To address these issues, we develop a multi-granularity facial makeup transfer and removal model with local-global collaboration. This model can apply and remove all makeup styles (i.e., light to extreme makeup) on a face image accurately. First, we design novel local discriminators for facial local patches divided by landmarks, in order to distinguish the accuracy of local makeup transfer. The local and global discriminators collaborate to effectively separate content and makeup style features, accurately handle transfer from coarse-grained global and fine-grained local perspectives, and adequately preserve facial identity. Then, we propose a novel loss function that ensures the consistency of local makeup styles, and maintains color, texture, and complex makeup patterns on the patches during the transfer process, generating a realistic appearance. Finally, we suggest dealing with an image’s facial and background regions independently, and separating them by introducing face parsing maps into both the generator and discriminators. This prevents the alteration of unrelated content and mitigates the negative impact of background information during makeup transfer and removal. Extensive experiments demonstrate our method’s effectiveness with light and extreme makeup styles.