Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Cognitive modeling: knowledge, reasoning and planning for intelligent characters
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Reactive Pedestrian Path Following from Examples
CASA '03 Proceedings of the 16th International Conference on Computer Animation and Social Agents (CASA 2003)
Group behavior from video: a data-driven approach to crowd simulation
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
A decision network framework for the behavioral animation of virtual humans
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
Seeing is believing: body motion dominates in multisensory conversations
ACM SIGGRAPH 2010 papers
Improving crowd behaviour for games and virtual worlds
Proceedings of the Fifth International Conference on the Foundations of Digital Games
Resolving local minima problem of potential field
Proceedings of the 9th ACM SIGGRAPH Conference on Virtual-Reality Continuum and its Applications in Industry
CAROSA: a tool for authoring NPCs
MIG'10 Proceedings of the Third international conference on Motion in games
Formation sketching: an approach to stylize groups in crowd simulation
Proceedings of Graphics Interface 2011
Environment-aware real-time crowd control
EUROSCA'12 Proceedings of the 11th ACM SIGGRAPH / Eurographics conference on Computer Animation
EUROSCA'12 Proceedings of the 11th ACM SIGGRAPH / Eurographics conference on Computer Animation
Environment-aware real-time crowd control
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation
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In this paper we present a data-driven approach for fitting behaviors to simulated pedestrian crowds. Our method annotates agent trajectories, generated by any crowd simulator, with action-tags. The aggregate effect of animating the agents according to the tagged trajectories enhances the impression that the agents are interacting with one another and with the environment. In a preprocessing stage, the stimuli which motivated a person to perform an action, as observed in a crowd video, are encoded into examples. Using the examples, non-linear, action specific influence functions are encoded into two-dimensional maps which evaluate, for each action, the relative importance of a stimulus within a configuration. At run time, given an agents stimuli configuration, the importance of each stimulus is determined and compared to the examples. Thus, the probability of performing each action is approximated and an action-tag is chosen accordingly. We fit behaviors to pedestrian crowds, thereby enhancing their natural appearance.