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Animating autonomous pedestrians

State-of-the-art computer graphics modeling and rendering techniques can be used to create photorealistic imagery of static objects, but they do not yet enable the automated animation of human beings with anywhere near as much fidelity. This thesis addresses the challenge. Our focus is the emulation of real pedestrians in urban environments. To this end, we develop an entirely autonomous pedestrian model that requires no centralized, global control whatsoever and is capable of performing a variety of activities in synthetic urban spaces, such as a virtual train station. The comprehensive artificial life modeling approach we adopt integrates motor, perceptual, behavioral, and cognitive components, making each of our virtual pedestrians a highly capable individual. 
To support a variety of natural interactions between pedestrians and their environment, we represent the latter using hierarchical data structures that efficiently execute the perceptual queries of pedestrians to sustain their behavioral responses and enable them to plan their actions on global and local scales. 
The animation system implemented using the above models enables us to run long-term simulations of pedestrians in fairly large urban environments without manual intervention. Real-time simulation can be achieved for over a thousand autonomous pedestrians. With each pedestrian under his/her own autonomous control, the characters imbue the virtual world with liveliness, social (dis)order, and a realistically complex dynamic. 
In addition to the automated animation of human activity in a virtual train station, we demonstrate our autonomous pedestrian simulator in the context of virtual archaeology for visualizing urban social life in reconstructed archaeological sites. Our pedestrian simulator has also served as the basis of a testbed for designing and experimenting with visual sensor networks in the field of computer vision.