Achieving Good Connectivity in Motion Graphs

Abstract

Motion graphs provide users with a simple yet powerful way to synthesize human motions. While motion graphbased synthesis has been widely successful, the quality of the generatedmotion depends largely on the connectivity of the graph and the quality of transitions in it. However, achieving both of these criteria simultaneously in motion graphs is difficult. Good connectivity requires transitions between less similar poses, while good motion quality results only when transitions happen between very similar poses. This paper introduces a new method for building motion graphs. The method first builds a set of interpolated motion clips, which contain many more similar poses than the original dataset. Using this set, the method then constructs a motion graph and reduces its size by minimizing the number of interpolated poses present in the graph. The outcome of the algorithm is a motion graph called a well-connected motion graph with very good connectivity and only smooth transitions. Our experimental results show that well-connected motion graphs outperform standardmotion graphs across a number of measures, result in very good motion quality, allow for high responsiveness when used for interactive control, and even do not require post-processing of the synthesizedmotions