3DFACENet 3D Facial Attractiveness Computation and Enhancement Network - Xie, Yuan (IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2025)
The development of facial editing, virtual makeup, AR/VR technologies and 3D games applications underscore the need for advanced 3D facial attractiveness research. However, due to the lack of 3D beauty face data and the complexity of handling 3D face data, 3D facial aesthetics research remains largely unexplored. To fill this gap, we propose 3DFACENet, an innovative system designed for the computation and enhancement of 3D facial attractiveness. Our approach employs a 3D facial reconstruction encoder to generate encoded vectors from images and a render module to obtain 3D face models. To minimize computational load, we innovatively propose an attractiveness computation module which leverages 3D shape and texture coefficients rather than 3D mesh models to access facial attractiveness, achieving state-of-the-art results. To balance aesthetic enhancement and identity preservation, we design a controllable beautification decoder. For the first time, we introduce the concept of "attractive centers", demonstrating that an individual's distance to these centers is significantly negatively correlated with their beauty scores. Our beautification decoder edits 3D facial coefficients towards these centers, achieving a significant and controllable enhancement in facial attractiveness. Extensive experiments on the SCUT-FBP5500 and MEBeauty dataset validate the effectiveness and feasibility of 3DFACENet