Multi-Layered Motion Graph
Motion capture is a popular method for generating realistic character animation. In most applications, a motion usually is prepared by manually blending existing captured motion clips to generate a desired motion clip. However, finding a good transition points manually for two motion clips is a time-consuming task and cannot be scaled up easily. Motion Graph is a technique that has been proposed to automate this process by finding suitable connection points and the corresponding transition motions between motion data. With this automatic procedure, motions captured separately can be smoothly connected while keeping the realism of the captured motions. However, most motion graph techniques only consider the transition of full-body motions in two motion clips, and therefore, the resulting motion .depends on the variety of motions available in the motion database. It is an important issue to be able to compose new motion clips as much as possible with given motion capture database. In this research, we propose a hierarchical motion graph structure called Multi-Layered Motion Graph. In this structure, we divide motion data into layers of parts depending on the articulated structure of human body, and then compute a motion graph for each part of the motion. We then combine these motion graphs into an interconnected hierarchical structure. In order to facilitate the composition of motions for different parts from different motion clips, we propose a new metric called Overall Motion Similarity to find reasonable composition of motions in run time. We also propose several rules about how to traverse the motion graphs in different layers to generate feasible motions. Furthermore, we have designed a scripting language called Motion Script to facilitate the specification and search of desirable animation to be generated. Our experimental results reveal that our method is able to compose animations that the original motion graph cannot generate in real time. Compared to the traditional motion graph method, our method is able to make good use of existing motion capture library to compose new motions in a systematic way.
Category: Autonomous Digital Actor, Computer Animations