DocumentCode :
1442439
Title :
Context-Aware Motion Diversification for Crowd Simulation
Author :
Qin Gu ; Zhigang Deng
Author_Institution :
Dept. of Comput. Sci., Univ. of Houston, Houston, TX, USA
Volume :
31
Issue :
5
fYear :
2011
Firstpage :
54
Lastpage :
65
Abstract :
Traditional crowd simulation models typically focus on navigational pathfinding and local collision avoidance. Little research has explored how to optimally control individual agents´ detailed motions throughout a crowd. A proposed approach dynamically controls agents´ motion styles to increase a crowd´s motion variety. The central idea is to maximize both the style variety of local neighbors and global style utilization while maintaining a consistent style for each agent that´s as natural as possible. To assist runtime diversity control, an offline preprocessing algorithm extracts primitive motions from a motion capture database and stylizes them. This approach can complement most high-level crowd models to increase realistic variety. Four experiment scenarios and a user evaluation demonstrate this approach´s superior flexibility compared to traditional random distribution of motion styles. The Web extra is a video demonstrating a military-march simulation.
Keywords :
computer graphics; ubiquitous computing; Web extra; context-aware motion diversification; crowd simulation model; local collision avoidance; military-march simulation; motion capture database; navigational pathfinding; offline preprocessing algorithm; Collision avoidance; Computational modeling; Computer science; Computer simulation; Context modeling; Motion control; Navigation; and user study; character animation; crowd simulation; motion diversification; motion variety; variety realism;
fLanguage :
English
Journal_Title :
Computer Graphics and Applications, IEEE
Publisher :
ieee
ISSN :
0272-1716
Type :
jour
DOI :
10.1109/MCG.2010.38
Filename :
5432119
Link To Document :
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