An algorithm for decomposing variations of 3D model

Abstract

In recent times, there has been an increasing number of people who are concerned about the virtual reality field. Parameterization of deformations of 3D models is a meaningful problem in theoretical research and application development of virtual reality. This paper proposes a technique for conditional decomposition of 3D model variations based on a given set of 3D observations of an object, along with a set of input strain weights. The proposed algorithm is conducted through an optimal iterative process with solving the non-negative least squares problem. The output of the technique is a set of base models corresponding to different types of strain. The result of the proposed technique allows the creation of a new 3D model variant of the object in a simple and visually observable way. The algorithm has been tested and proven effective on data that are 3D face models created from the Japanese Female Facial Expression (JAFFE) dataset with labeled expression weights.