A Facial Expression Parameterization by Elastic Surface Model

Abstract

We introduce a novel parameterization of facial expressions by using elastic surface model. The elastic surface model has been used as a deformation tool especially for nonrigid organic objects. The parameter of expressions is either retrieved from existing articulated face models or obtained indirectly by manipulating facial muscles. The obtained parameter can be applied on target face models dissimilar to the source model to create novel expressions. Due to the limited number of control points, the animation data created using the parameterization require less storage size without affecting the range of deformation it provides. The proposed method can be utilized in many ways: (1) creating a novel facial expression from scratch, (2) parameterizing existing articulation data, (3) parameterizing indirectly by muscle construction, and (4) providing a new animation data format which requires less storage.

Design of a Three-dimensional Face Recognition System

“…Currently, there are three most common 3D data acquisition methods: first of all, measure facial data through digitizer--facial measurements are obtained directly from digitizer, or structured light sensor, or tomography, and 3D model will be obtained after the measured data is digitized [6]; second, based on general model method, general facial model is generated artificially, and faces are structured through B-spline and other methods, then surface rendering texture information is filled [7][8][9]; third, 3D facial model is constructed on the basis of a set of two-dimensional images [10,11]. Applying the idea of structured light, the paper establishes a platform able to obtain facial 3D data with a laser lighting source and an ordinary camera, and completes a data acquisition system through triangulation principle.…”

Expression Cloning Based on Anthropometric Proportions and Deformations by Motion of Spherical Influence Zones

“…Also, the adopted model of influence zones presents some limitations which will be discussed later. In their Expression Cloning work, Noh and Neumann [13] applied similar deformation techniques to transfer different types of expressions between two virtual characters with distinct mesh topology, serving as a base for many other works [10], [14], [16], [17], [18], [19], [20]. Some of these works used models with different facial features, similar to Noh and Neumann [13], and some used only human models, but, in order to transfer the expressions it was necessary to find dense correspondences between the models using volume morphing and a cylindrical projection to apply the deformations by RBF.…”

On the simulation of expressional animation based on facial MoCap

“…The foregoing methods summarized the current research of cross-mapping for facial MoCap data. Accordingly, researchers pay attention to mapping expressions for different facial geometrical models, called "Expression re-targeting" or "Expression clone" [13][14][15][16][17][18][19][20].…”