The team developed what it calls a curvature-fused graph attention network, or CF-GAT, that can predict facial landmarks directly from raw 3D point clouds. Point clouds are sets of data points in 3D space, and in this case they are being used to represent facial geometry without relying on conventional 2D image textures or template-based models. The researchers said existing 3D facial landmark detection methods have often been constrained by a lack of large, accurately annotated 3D face datasets, pushing many systems to depend on 2D texture assistance or synthetic 3D faces.