FAST-Net: A Coarse-to-fine Pyramid Network for Face-Skull Transformation

Face-skull transformation, i.e., shape transformation between facial surface and skull structure, has a wide range of applications in various fields such as forensic facial reconstruction and craniomaxillofacial (CMF) surgery planning. However, this transformation is a challenging task due to the significant differences between the geometric topologies of the face and skull shapes. In this paper, we propose a novel coarse-to-fine face-skull transformation network(i.e., FAST-Net) that has a pyramid architecture to gradually improve the transformation level by level. Specifically, using face-to-skull transformation for instance, in the first pyramid level, we use a point displacement sub-network to predict a coarse skull shape of point cloud from a given facial shape of point cloud with a skull template of point cloud as prior information. In the following pyramid levels, we further refine the predicted skull shape by first dividing the skull shape together with the given facial shape into different sub-regions, individually feeding the regions to a new sub-network, and merging the outputs as a refined skull shape. Finally, we generate a surface mesh model for the final predicted skull point cloud by non-rigidly registration with a skull template. Experimental results show that our method achieves the state-of-the-art performance on the task of face-skull transformation.