The development of AR/VR technologies, facial editing, virtual makeup and 3D games applications underscore the need for advanced 3D facial attractiveness research. In previous chapters, we introduced the 3D facial reconstruction technology and shown how to extract 3D geometric features. Besides, we provided detailed comparison between 2D features and 3D features to show the necessity and feasibility of 3DFBA. In this chapter, we introduce our previous work, 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 vector from images and a render module to obtain 3D face models. 3DFACENet includes two crucial components: an attractiveness computation module and a beautification decoder. To minimize computational load, our attractiveness computation module leverage 3D shape and texture coefficients to access facial attractiveness, achieving state-of-the-art results using a 180-dimensional vector. We introduce the concept of “attractiveness centers,” clustering high-attractiveness individuals by gender to guide the beautification process. The beautification decoder modifies the 3D facial coefficients toward these centers, resulting in significant and controllable enhancements in facial attractiveness. Extensive experiments and analyses on the SCUT-FBP5500 dataset and the MEBeauty dataset validate the effectiveness and feasibility of 3DFACENet.