Natural Pose Generation from a Reduced Dimension Motion Capture Data Space

Human animation from motion capture data is typically limited to whatever movement was performed by the actor. A method to create a wider range of motion in the an animation utilizes the motion capture database to synthesize new poses. This paper proposes a method to venerate original natural poses based on the characteristics of natural poses based on motion capture data. Principal Component Analysis is used to transform the data into a reduced dimensional space. An unconstrained pose data set is created by calculating the position of the human skeleton based on the reduced dimensional space. Constrained pose data can be created using interpolation and iteration on the unconstrained pose data. We show some example results of the venerated poses and compare these poses to poses created with iterative inverse kinematics methods. Results show that our method is more accurate and more natural than iterative inverse kinematics methods.