Fitting a Morphable Model to Pose and Shape of a Point Cloud

The paper addresses the task of fitting a morphable head model to a dense, unstructured and untextured point cloud. The problem is typically approached in a multi-step process, comprising of generic nonrigid registration, conversion to the model’s topology and fitting of the model. Here, a direct approach is proposed where the morphable model is fitted to the point cloud itself in an optimization process following the Iterative Closest Points framework. Along with shape parameters, rigid pose and scale are estimated explicitly which leads to better fitting results than shape estimation alone. A compact formulation of a cost function is proposed, applicable also in the case of a model comprising of multiple, independent sub-models each of which describes a region of the face. The use of the algorithm for other tasks such as adding depth to mugshot-style face photos is discussed.

[1]  Hans-Peter Seidel,et al.  Head shop: generating animated head models with anatomical structure , 2002, SCA '02.

[2]  Andrew W. Fitzgibbon,et al.  3D head tracking using non-linear optimization , 2003, BMVC.

[3]  Zoran Popovic,et al.  The space of human body shapes: reconstruction and parameterization from range scans , 2003, ACM Trans. Graph..

[4]  Pascal Fua,et al.  Regularized Bundle-Adjustment to Model Heads from Image Sequences without Calibration Data , 2000, International Journal of Computer Vision.

[5]  Sebastian Thrun,et al.  The Correlated Correspondence Algorithm for Unsupervised Registration of Nonrigid Surfaces , 2004, NIPS.

[6]  Peter Eisert,et al.  Fast nonrigid mesh registration with a data-driven deformation prior , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[7]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Sami Romdhani,et al.  Optimal Step Nonrigid ICP Algorithms for Surface Registration , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Hans-Peter Seidel,et al.  Fitting a Morphable Model to 3D Scans of Faces , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[10]  Sami Romdhani,et al.  Estimating 3D shape and texture using pixel intensity, edges, specular highlights, texture constraints and a prior , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  Andrew W. Fitzgibbon,et al.  Reconstructing High Quality Face-Surfaces using Model Based Stereo , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[12]  Sebastian Thrun,et al.  SCAPE: shape completion and animation of people , 2005, SIGGRAPH 2005.

[13]  Helmut Pottmann,et al.  Registration of point cloud data from a geometric optimization perspective , 2004, SGP '04.

[14]  Thomas Vetter,et al.  A morphable model for the synthesis of 3D faces , 1999, SIGGRAPH.

[15]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..

[16]  Philosophisch-Naturwissenschaftlichen Fakult,et al.  Face Image Analysis using a Multiple Features Fitting Strategy , 2005 .

[17]  Raghu Machiraju,et al.  Silhouette-Based 3D Face Shape Recovery , 2003, Graphics Interface.

[18]  Thomas Vetter,et al.  Expression invariant 3D face recognition with a Morphable Model , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[19]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[20]  Gérard G. Medioni,et al.  Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[21]  Thomas Vetter,et al.  Face Recognition Based on Fitting a 3D Morphable Model , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  K. Ikeuchi,et al.  Iterative Estimation of Rotation and Translation using the Quaternion , 1995 .