GeoFAME: Restoring Beautified Faces via Geometry Correction and Frequency-Aware Multi-Expert Learning

With the widespread use of social media platforms and intelligent portrait editing tools, facial beautification has become a common form of image manipulation, raising potential concerns about the authenticity of face images and identity consistency. Existing studies mainly focus on detecting beautified face images, while how to accurately recover their corresponding natural faces remains underexplored. Operations such as face slimming, eye enlargement, skin smoothing, and skin whitening simultaneously alter facial geometry, local texture, and color distribution, making facial beautification restoration a challenging task. To address this problem, we propose GeoFAME, a Geometry-Corrected Frequency-Aware Multi-Expert Network for facial beautification restoration. GeoFAME first alleviates non-rigid deformations introduced by beautification through geometric correction, and then employs a Laplacian pyramid to decompose multi-scale frequency information, with structure, texture, and color experts performing targeted restoration. Meanwhile, a spatially adaptive router dynamically fuses the expert outputs according to the restoration requirements of different facial regions. Extensive experiments demonstrate that GeoFAME effectively restores structural, textural, and color changes caused by both single and composite beautification operations, while achieving favorable restoration performance and cross-dataset generalization under different beautification intensities.