We propose an approach to construct realistic 3D facial morphable models (3DMM) that allows an intuitive facial attribute
 editing workflow. Current face modeling methods using 3DMM suffer from the lack of local control. We thus create a 3DMM by
 combining local part-based 3DMM for the eyes, nose, mouth, ears, and facial mask regions. Our local PCA-based approach
 uses a novel method to select the best eigenvectors from the local 3DMM to ensure that the combined 3DMM is expressive
 while allowing accurate reconstruction. The editing controls we provide to the user are intuitive as they are extracted from
 anthropometric measurements found in the literature. Out of a large set of possible anthropometric measurements, we filter the
 ones that have meaningful generative power given the face data set. We bind the measurements to the part-based 3DMM through
 mapping matrices derived from our data set of facial scans. Our part-based 3DMM is compact yet accurate, and compared to
 other 3DMM methods, it provides a new trade-off between local and global control. We tested our approach on a data set of 135
 scans used to derive the 3DMM, plus 19 scans that served for validation. The results show that our part-based 3DMM approach
 has excellent generative properties and allows intuitive local control to the user.


We propose an approach to construct realistic 3D facial morphable models (3DMM) that allows an intuitive facial attribute editing workflow by selecting the best sets of eigenvectors and anthropometric measurements. Proposes a piecewise morphable model for human face meshes and also proposes a mapping between anthropometric measurements of the face and the parameters of the model in order to synthesize and edit faces with desired attributes.  This paper describes a method of part-based morphable facial model allowing for localized user control.