Motion Normalization Method based on an Inverted Pendulum Model for Clustering

Abstract

In many creative industries, such as the animation, movie, and game industries, artists often make good use of motion data to create their works by retrieving a particular motion from motion-capture data and reusing it. A large data-base of human motion is difficult to use unless the motion data are organized according to the type of motion. Although there have been many results for clustering motion capture data, many variations in the motion data complicate the clustering of data by making one type of motion numerically similar to other types of motions. To improve the motion clustering performance, we present a novel physically based motion normalization method that reduces ambiguous elements of motions, so that motions that have different semantics can be differentiated. The normalized motion data generated by our method can be used as input to existing clustering algorithms and improves the results.