Face Modeling and Editing with Statistical Local Feature Control Models

This article presents a novel method based on statistical facial feature control models for generating realistic controllable face models. The local feature control models are constructed based on the exemplar 3D face scans. We use a three-step model fitting approach for the 3D registration problem. Once we have a common surface representation for examples, we form feature shape spaces by applying a principal component analysis (PCA) to the data sets of facial feature shapes. We compute a set of anthropometric measurements to parameterize the exemplar shapes of each facial feature in a measurement space. Using PCA coefficients as a compact shape representation, we approach the shape synthesis problem by forming scattered data interpolation functions that are devoted to the generation of desired shape by taking the anthropometric parameters as input. The correspondence among all exemplar face textures is obtained by parameterizing a 3D generic mesh over a 2D image domain. The new feature texture with desired attributes is synthesized by interpolating the exemplar textures. With the exception of an initial tuning of feature point positions and assignment of texture attribute values, our method is fully automated. In the resulting system, users are assisted in automatically generating or editing a face model by controlling the high-level parameters.