Crowd and group animation

Thalmann, Daniel; Kermel, Laurent; Opdyke, William F.; Regelous, Stephen

doi:10.1145/1198555.1198671

Cited by 9 publications

(6 citation statements)

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“…Virtual Crowd is a wide topic that raises numerous problems including population design, control, simulation or rendering and was surveyed in recent books [Thalmann and Raupp Musse 2007;Pelechano et al 2008] and tutorials [Thalmann et al 2005;Halperin et al 2009]. This overview focuses on crowd simulation, the objective of which can be restrictively defined as computing global locomotion trajectories to achieve goal-driven collision-free navigation for crowds of walkers.…”

Section: Related Workmentioning

confidence: 99%

A synthetic-vision based steering approach for crowd simulation

et al. 2010

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Section: Related Workmentioning

confidence: 99%

A synthetic-vision based steering approach for crowd simulation

et al. 2010

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“…It was surveyed in recent books [Thalmann and Raupp Musse 2007;Pelechano et al 2008] and tutorials [Thalmann et al 2005;Halperin et al 2009]. This overview focuses on crowd simulation, whose objective can be restrictively defined as computing global locomotion trajectories to achieve goal-driven collision-free navigation for crowds of walkers.…”

Section: Related Workmentioning

confidence: 99%

A synthetic-vision based steering approach for crowd simulation

Ondřej

Pettré

Olivier

et al. 2010

ACM SIGGRAPH 2010 Papers

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Figure 1: Animations resulting from our simulations. Emergent self-organized patterns appear in real crowds of walkers. Our simulations display similar effects by proposing an optic flow-based approach for steering walkers inspired by cognitive science work on human locomotion. Compared to previous approaches, our model improves such an emergence as well as the global efficiency of walkers traffic. We thus enhance the overall believability of animations by avoiding improbable locking situations. AbstractIn the everyday exercise of controlling their locomotion, humans rely on their optic flow of the perceived environment to achieve collision-free navigation. In crowds, in spite of the complexity of the environment made of numerous obstacles, humans demonstrate remarkable capacities in avoiding collisions. Cognitive science work on human locomotion states that relatively succinct information is extracted from the optic flow to achieve safe locomotion. In this paper, we explore a novel vision-based approach of collision avoidance between walkers that fits the requirements of interactive crowd simulation. By simulating humans based on cognitive science results, we detect future collisions as well as the level of danger from visual stimuli. The motor-response is twofold: a reorientation strategy prevents future collision, whereas a deceleration strategy prevents imminent collisions. Several examples of our simulation results show that the emergence of self-organized patterns of walkers is reinforced using our approach. The emergent phenomena are visually appealing. More importantly, they improve the overall efficiency of the walkers' traffic and avoid improbable locking situations.

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“…One of the most challenging issues in computer graphics is to generate human models and to perform the simulations in a virtual space. In order to generate virtual human models, many technologies should be integrated such as motion capture, face and body modeling, motion control, groups and crowd simulation, interaction with 3D objects, and so on (Magnenat- Thalmann and Thalmann (2004)). In addition, human behavior model should be divided into some categories such as individual, groups and crowd (Thalmann (2004)).…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…In order to generate virtual human models, many technologies should be integrated such as motion capture, face and body modeling, motion control, groups and crowd simulation, interaction with 3D objects, and so on (Magnenat- Thalmann and Thalmann (2004)). In addition, human behavior model should be divided into some categories such as individual, groups and crowd (Thalmann (2004)). Reynolds (1999) decomposed the motion of human behavior into 3 levels: action selection, steering, and locomotion.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Virtual human simulation on memory acquisition and walking with the memory

2017

Syme, G., Hatton MacDonald, D., Fulton, B. And Piantadosi, J. (Eds) MODSIM2017, 22nd International Congress on Modelling and Si

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In virtual simulations, one of the most difficult tasks is how to model human behavior and how to validate the model by performing the simulations. Actually, many researchers have tried to model virtual humans for the behavior. Some researchers focused on how to decide the path for each person to move in a crowded situation. Others considered that human behavior cannot be generated with just one model, and the model should be categorized to some patterns such as individual, groups and crowd. Then, human actions are generated by applying each model according to the situation. In crowded situation, people might collide with each other if they move their own way without considering other person's path. Then, collision avoidance model was also investigated. In addition, there are some simulations considering real situations such as crowded train station or epidemics of infectious disease.

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Crowd and group animation

Cited by 9 publications

References 0 publications

A synthetic-vision based steering approach for crowd simulation

A synthetic-vision based steering approach for crowd simulation

A synthetic-vision based steering approach for crowd simulation

Virtual human simulation on memory acquisition and walking with the memory

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