Interactive computer-graphics simulations offer a proven method for learning complex relationships and procedures. Airplane simulators, for example, tutor pilots through sophisticated tasks until all procedures are performed safely and efficiently. Of particular importance to these applications are simulations that are consistent with the real world. In medicine, for example, physicians can master fine motor skills within physiologically consistent simulations without endangering the lives of patients. However, the simulations in use today can only emulate reactive physical systems. Broader application of computer simulation in medicine, engineering, art, and entertainment requires simulation of active systems such as robotic devices, humans, and animals that act as well as react within physically simulated environments. A study of simulations for vehicle and workspace design, for example, has revealed a pervasive need for digital human models that can accomplish tasks within physically simulated environments.
Computer graphics today often simulates humans as lifeless mechanical structures (rag dolls) that do no more than crumple to the ground. Or it relies on motion-capture techniques (motion graphs) that generate physically inconsistent motions whenever simulations depart from a few motion-captured scenarios. In contrast, physically based simulation generates consistent motions, but the motions are often unnatural and difficult to create. This study applies the mathematics of modern control theory to establish a more rigorous foundation for control of such motions in interactive computer simulations. It simulates natural motions by stabilizing high-quality motion-capture data, transforming individual motion into a more general action that can be applied in many different circumstances. A single action may describe short tasks such as stepping, turning, walking, and jumping. More complex behaviors are assembled through composition, by concatenating one action after another.
