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We study how we can automatically create a data structure that represents the walkable surfaces in such an environment, and how it can be updated dynamically and efficiently when it changes. We refer to this structure as a navigation mesh. This mesh enables efficient crowd simulation, which is our next topic of research. We study and develop a crowd simulation framework and its components, which ranges from global (AI) planning to local animation. We create models for realistic crowd behaviors, which includes studying how (groups of) people move and avoid collisions in such environments, based on agent profiles and semantics (such as terrain annotations).

On Streams and Incentives: A Synthesis of Individual and Collective Crowd Motion

Dealing with low- and high-density crowds.


We present a crowd simulation model that combines the advantages of agent-based and flow-based paradigms while only relying on local information. Our model can handle arbitrary and dynamically changing crowd densities, and it enables agents to gradually interpolate between individual and coordinated behavior. Our model can be used with any existing global path planning and local collision-avoidance method. We show that our model reduces the occurrence of deadlocks and yields visually convincing crowd behavior for high-density scenarios while maintaining individual agent behavior at lower densities.



The steps of our algorithm are visualized in the movie below.

Visualized steps in the algorithm.