Straight line walking animation based on kinematic generalization that preserves the original characteristics

The most prominent problems in utilizing the rotoscopy data for human walking animation can be summarized into two: Preservation of the original motion characteristics in the generalization process and the Constraint Satisfaction. Generalization is the process of producing the step of an arbitrary body and step length out of the original measured step which is of one particular subject and step length. If we lose much of the original style in the generalization, it would be meaningless to use the measured data. We present a generalization technique that keeps the original motion characteristics as much as possible. Two types of generalization are considered. The one is the body condition generalization, which handles the differences between the two bodies. The ratio between the corresponding segments of the two bodies may not be uniform, which makes this generalization complicated. The other one is the step length generalization, which provides the steps with different step lengths of the same subject. These two generalizations are combined together to generate a step of arbitrary subject and step length. The constraint satisfaction is enforced inside of our generalization process. Therefore the only thing that concerns us is the quality of the generalization. In our work, the preservation of the original characteristics is considered as the criteria determining the quality of the generalization. We prove that our generalization scheme actually preserves the characteristics of the original walk.