Interactive Knowledge Integration in 3D Cloth Animation with Intelligent Learning System

In this paper, we focus on the parameter identification problem, one of the most essential problems in the 3D cloth animation created by multimedia software. We present a novel interactive parameter identification framework which integrates the industry knowledge. The essential of this paper is that we design a hybrid intelligent learning system using statistical analysis of kawabata evaluation system(KES) data from fabric industry database, fuzzy system and radial basis function(RBF) neural networks. By adopting our method the 3D cloth animator can interactively identify the parameters of cloth simulation with subjective linguistic variables while in the past decades it is very difficult for cloth animators to tune the parameters. We solve the 3D cloth parameter problem using the intelligent knowledge integration method for the first time in the multimedia and graphics research area and our method is applied to the most popular 3D tool Maya. The experimental results illustrate the practicability and expansibility of this method.