Seung In Park

Modeling social groups in crowds using common ground theory

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.

Simulating and animating social dynamics: embedding small pedestrian groups in crowds

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

A real-time system for crowd rendering: parallel LOD and texture-preserving approach on GPU

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.

Modeling Agent Social Joint Actions via Micro and Macro Coordination Strategies

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 micro-coordination 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-groups 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.

Performance analysis of a novel GPU computation-to-core mapping scheme for robust facet image modeling

Modern graphics processing units (GPUs) are commodity data-parallel coprocessors capable of high performance computation and data throughput. It is well known that the GPUs are ideal implementation platforms for image processing applications. However, the level of efforts and expertise to optimize the application performance is still substantial. This paper investigates the computation-to-core mapping strategies to probe the efficiency and scalability of the robust facet image modeling algorithm on GPUs. Our fine-grained computation-to-core mapping scheme achieves a significant performance gain over the standard pixel-wise mapping scheme. With in-depth performance comparisons across the two different mapping schemes, we analyze the impact of the level of parallelism on the GPU computation and suggest two principles for optimizing future image processing applications on the GPU platform.

A Crowd Modeling Framework for Socially Plausible Animation Behaviors

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.

Modeling small group behaviors in large crowd simulation

Large crowds are seldom made up solely of a mass of individuals. They typically also include large collections of small groups. However the most of existing approaches to crowd modeling treat a crowd either as a collection of isolated individuals, each maintaining its own goal, or as an aggregated entity in which large number of individuals share the same goal and behavior pattern.