Dynamic Models of Human Behavior
University of Washington
Department Of Computer Science and Engineering
Auditorium (NSH 1305)
Refreshments 11:15 am
Talk 11:30 am
In this talk I will describe two models of human locomotion that attempt to describe both micro (stylistic variation of locomotion) and macro (complex crowd behavior) motion behavior patterns of humans through a set of tuned differential equations.
The first model of human locomotion incorporates several important aspects of human biology, including relative preferences for using some muscles more than others, elastic mechanisms at joints due to the mechanical properties of tendons, ligaments, and muscles, and variable stiffness at joints depending on the task. When used in a spacetime optimization framework, the parameters of this model define a wide range of styles of natural human movement. Due to the complexity of biological motion, these style parameters are too difficult to design by hand. To address this, I will describe the process of Nonlinear Inverse Optimization, an algorithm for estimating optimization parameters from motion capture data. We show how salient physical parameters cam be extracted from a single short motion sequence. Once captured, this representation of style is extremely flexible: motions can be generated in the same style but performing different tasks, and styles may be edited to change the physical properties of the body.
The second part of the talk will present a real-time model of crowd dynamics that is based on the continuum computations instead of per-agent simulations. This formulation yields a set of continuous velocity and potential fields that guide all people simultaneously. A dynamic potential field integrates both local collision avoidance and global navigation, efficiently solving for smooth realistic motion for large crowds without the need for collision detection. Simulations created with our system run at interactive rates, exhibit smooth flow under a variety of conditions, and naturally exhibit emergent phenomena that have been observed in real crowds.
This talk describes joint work with Karen C. Liu, Aaron Hertzmann, Adrien Treuille, and Seth Cooper.
Zoran Popovic is an Associate Professor in computer science at