Behavioral Animation of Crowds
One of the important characteristics of behavioral animation is its ability to reduce the workload on the animators. This is achieved by letting a behavioral model automatically take care of the low-level details of the animation, freeing the animator to
- PDF / 3,670,206 Bytes
- 58 Pages / 439.37 x 666.142 pts Page_size
- 80 Downloads / 203 Views
Behavioral Animation of Crowds
5.1 Introduction One of the important characteristics of behavioral animation is its ability to reduce the workload on the animators. This is achieved by letting a behavioral model automatically take care of the low-level details of the animation, freeing the animator to concentrate on the big picture. Freeing the animator from low-level animation details is even more important when dealing with crowds. An animator manually creating a crowd animation is overwhelmed not only by the large number of animated entities, but also by the interactions between them. For instance, manually animating ten human characters walking in a room requires more than ten times the work of animating a single character walking in a room, because the animator must deal with new problems like the possibility of collisions between characters. This observation reinforces the importance of research on behavioral animation models for crowds. Indeed, the entire field of behavioral animation has its origins strongly connected to crowds. This can be seen in the next section, where a review of the field is presented. Section 5.3 describes two behavioral models for crowds that have been successfully used in crowd simulations. Then, Sect. 5.4 discusses crowds navigation, which is one of the most important behaviors for crowd simulations.
5.2 Related Work The seminal work by Reynolds [Rey87] is considered by many the first one in the field of behavioral animation. It presented a method to animate large groups of entities called boids, which present behaviors similar to those observed in flocks of birds and schools of fishes. Reynolds started from the premise that the group behavior is just the result of the interaction between the individual behavior of the group members. Therefore, it would suffice to simulate the reasonably simple boids individually, and the more complex flocking behavior would emerge from the interaction between them. D. Thalmann, S.R. Musse, Crowd Simulation, DOI 10.1007/978-1-4471-4450-2_5, © Springer-Verlag London 2013
111
112
5
Behavioral Animation of Crowds
The boid model proposed by Reynolds consisted of three simple rules: (i) avoiding collisions with other boids, (ii) matching the velocity of nearby boids, and (iii) flying toward the center of the flock. Results presented by the author demonstrated that, as he originally expected, the interaction between creatures whose behavior was governed by these three simple rules leads to the emergence of a much more complex flocklike behavior. Tu and Terzopoulos [TT94] created a realistically rich environment inhabited by artificial fishes. The complexity of the undersea life, including interactions between fishes like predators hunting preys, mating, and schooling, was obtained by modeling the behavior of individual fishes: group behaviors emerged as the individuals interacted. In this sense, this work is similar to the work by Reynolds discussed in the previous paragraphs. It must be emphasized, though, that the artificial fishes created by T
Data Loading...
