Local control editing paradigms for part‐based 3D face morphable models

Abstract

AbstractWe 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 a 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 principal component analysis (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. We provide different editing paradigms, all designed from the analysis of the data set. Some use anthropometric measurements from the literature and others allow the user to control the dominant modes of variation extracted from the data set. 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 the user intuitive local control.