3D face data acquisition and modelling based on an RGBD camera matrix

Abstract

This paper describes a novel system for building morphable 3D head models. In contrast to most of the previous approaches that need several seconds to capture each scan, we acquire the data using a matrix of calibrated RGBD cameras, enabling real time face scanning. We localize the face and it’s 68 characteristic points on an orthogonal projection image, and use the detected points to align multiple scans. We use a Delaunay triangulation of the 68 characteristic points to obtain dense head shapes with point to point correspondence across all 3D head shapes. In the last step we create a morphable model in a way that is similar to the original procedure by Blanz and Vetter. We demonstrate the functionality of our model, created on just five people, in a real-time application. The novelty of this article lies mostly in the method of defining correspondences of the characteristic points in 3D, that leads to a realistic three-dimensional model and blendshapes.

Publication
IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)
Michal Daniluk
Michal Daniluk
Research Scientist

My research interests include graph representation learning, recommendation systems, behavioral user representations, NLP.