Social interaction and group coordination are important factors in the simulation of human crowd behavior. To date, few simulation methods have been informed by models of human group behavior from the social science studies. In this paper we advance a computational model informed by Common Ground (CG) Theory that both inherits the social realism provided by the CG model and is computationally tractable for a large number of groups and individuals. The task of navigation in a group is viewed as performing a joint activity among agents, which requires effective coordination among group members. Our model includes both macro and micro coordination, addressing the joint plans, and the actions for coordination respectively. These coordination activities and plans inform the high-level route and walking strategies of the agents. We demonstrate a series of studies to show the qualitative and quantitative differences in simulation results with and without incorporation of the CG model.

Abstract. In modern games, rendering a massive scene with a large number of animated character is imminent and a very challenging task. In this paper, we present a real-time crowd rendering system on GPUs with a special focus on how to preserve texture appearance in progressive LOD-based mesh simplification algorithms. Our results show that the proposed parallel LOD approach can get up to 5.33 times of speedup compared with the standard pseudo-instancing approach.

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We present a crowd model informed by common ground theory to accommodate high‐level socially aware behavioral realism of characters in crowd simulations. In our approach, group members maintain group cohesiveness by communicating and adapting their behaviors to each other. The resulting character behaviors in animations form a consequential chain interpreted as a coherent story by observers. We demonstrate that our model produces more believable animations from the viewpoint of human observers through a series of user studies. Copyright © 2013 John Wiley & Sons, Ltd.

Abstract-This paper presents a multi-agent model for large crowd simulations that addresses the need for socially plausible coordination behavior. A computational model for multi-agent coordination informed by well-established common ground theory is proposed. We introduce the idea of macro-and microcoordination strategies that allow agent-based simulations to adapt to different domains. Our agent model allows the selection of appropriate behaviors based on the spatiotemporal conditions of the agent-group's environment. By showing that different micro-coordination strategies of individual groups has an influence on the overall distribution of a crowd, we demonstrate the importance of incorporating such models into multi-agent simulations of large crowd behaviors.

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Abstract. This paper presents a framework for crowd modeling that produces socially plausible animation behaviors. Our high-level behavioral model is able to produce appropriate animated behavior that includes synchronized body-orientation and gesture of individual actors within the simulation. Because the model operationalizes a well-founded social-linguistic Common Ground (CG) theory of human interaction, the behavior chains form meaningful interactions among the actors. The model includes micro-behaviors relating to CG theory, and macro-behavior relating to the animation context. This allows reuse of the micro-behaviors as animation contexts change and flexible adaptation to different animation contexts.

In this paper, we discuss our implementation of a graphics hardware acceleration of the Vector Coherence Mapping vision processing algorithm. Using this algorithm as our test case, we discuss our optimization strategy for various vision processing operations using NVIDIA's new CUDA programming framework. We also demonstrate how flexibly and readily vision processing algorithms can be mapped onto massively parallelized GPU architecture. Our results and analysis show the GPU implementation exhibits a performance gain of more than 40-fold of speedup over state-of-art CPU implementation of VCM algorithm.