This thesis addresses the difficult open problem in computer graphics
of autonomous human modeling and animation, specifically of emulating
the rich complexity of real pedestrians in urban environments.
We pursue an artificial life approach that integrates motor, perceptual, behavioral, and cognitive components within a model of pedestrians as highly capable individuals. Our comprehensive model features innovations in these components, as well as in their combination, yielding results of unprecedented fidelity and complexity for fully autonomous multi-human simulation in large urban environments. Our pedestrian model is entirely autonomous and requires no centralized, global control whatsoever.
To animate a variety of natural interactions between numerous pedestrians and their environment, we represent the environment using hierarchical data structures, which efficiently support the perceptual queries of the autonomous pedestrians that drive their behavioral responses and sustain their ability to plan their actions on local and global scales.
The animation system that we implement using the above models enables us to run long-term simulations of pedestrians in large urban environments without manual intervention. Real-time simulation can be achieved for well over a thousand autonomous pedestrians. With each pedestrian under his/her own autonomous control, the self-animated characters imbue the virtual world with liveliness, social (dis)order, and a realistically complex dynamic.
We demonstrate the automated animation of human activity in a virtual train station, and we employ our pedestrian simulator in the context of virtual archaeology for visualizing urban social life in reconstructed archaeological sites. Our pedestrian simulator is also serving as the basis of a testbed for designing and experimenting with visual sensor networks in the field of computer vision.