DeBeauty: A Joint Framework for Facial Beautification Removal Based on Spatial Collaborative Adaptation and Hyperplane Relocation

Facial beautification removal presents a formidable inverse challenge due to the inherent diversity and unpredictability of beautification processes. Current methodologies often fall short in effectively restoring facial structural alterations and preserving texture features during makeup removal. This paper introduces DeBeauty, an innovative joint framework for facial beautification removal, comprising two primary workflows: Adversarial De-Makeup Flow (ADF) and Relocation Deformation Flow (RDF). ADF incorporates Multi-Level Perception Collaborative Discrimination (MLPCD) and Discriminator-Guided Spatial Adaptive Multi-Scale Attention (SAMA), which enhance the detection of subtle makeup and facilitate the comprehensive removal of extensive makeup while preserving facial texture. RDF introduces a Hyperplane Relocation Strategy that adjusts the latent code of the input face to align with the original structural distribution. Experimental evaluations on the newly proposed Multivariate Beautified Face dataset demonstrate that this approach effectively restores the original color and structural context of the face while preserving essential facial features, achieving state-of-the-art performance.