Pose synthesis using the inverse of jacobian matrix learned from examples

Abstract

This paper presents a method of pose synthesis based on a low-dimensional space and a set of characteristics of motion learned from examples. This method consists of two phases: learning and synthesis. In the learning phase, a low-dimensional and discrete representation of the space of natural poses is constructed by using a Self Organizing Map (SOM). Meanwhile, a set of matrices is extracted from the motion data. These matrices describe how the poses change with the end-effectors' positions, and play a key role in synthesizing natural looking results. In the synthesis phase, a lightweight algorithm based on the learned parameters is used. The synthesis process is very efficient because there is no timeconsuming calculation, like numeric optimization or matrix inverting. Compared with other methods, our method not only can produce natural looking poses in real-time, but also works well with constraints positioned in a larger range. We apply our method in applications of interactive pose editing, real-time motion modification, and pose reconstruction from image. The results have proven the robustness and effectiveness of our method. © 2007 IEEE.