A history of crowd simulation: the past, evolution, and new perspectives

Musse, Soraia Raupp; Cassol, Vinícius Jurinic; Thalmann, Daniel

doi:10.1007/s00371-021-02252-w

Cited by 16 publications

(9 citation statements)

References 109 publications

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“…The area of group perception has grown in recent years. Group perception is essential for learning about group patterns [McDonnell et al 2008, Molina et al 2021, Musse et al 2021]. However, these results did not take into account the effect of UV and the use of VR in these scenarios, which is what we want to explore in our work.…”

Section: Related Workmentioning

confidence: 97%

“…The authors performed perceptual experiments, which explored how different anomalies have relative importance. Regarding the interactivity of experiments with VR [Schwind et al 2018, Pelechano and Allbecky 2016, Musse et al 2021, in the work of [Seymour et al 2017] the authors observed the effect of UV theory on user interaction with the photo-realistic human avatar without just looking at its appearance. The area of group perception has grown in recent years.…”

Section: Related Workmentioning

confidence: 99%

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How do we perceive Characters? An Analysis of Human Perception in Still Images, Animations and VR Scenarios

Araujo

Dalmoro

Geiss

et al. 2022

Anais Estendidos Do XXI Simpósio Brasileiro De Jogos E Entretenimento Digital (SBGames Estendido 2022)

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Virtual characters can elicit an uncomfortable sensation usually known as Uncanny Valley (UV). Evidence suggests that animation exacerbates the UV effect. This paper revisits the UV hypothesis to assess its effects on people’s perception of virtual characters, to try to answer the questions “What happens to the perceptual comfort if, in addition to images and videos of characters, we include those characters in an interactive Virtual Reality (VR) environment?”, and “Considering the VR environment, do people feel equally comfortable if we increase the number of characters?”. The results indicate that there are differences in the perception of scenarios with low and high densities.

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Section: Related Workmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

How do we perceive Characters? An Analysis of Human Perception in Still Images, Animations and VR Scenarios

Araujo

Dalmoro

Geiss

et al. 2022

Anais Estendidos Do XXI Simpósio Brasileiro De Jogos E Entretenimento Digital (SBGames Estendido 2022)

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“…The models showed that different behavioral processes could lead to similar aggregate outcomes, such as consensus and polarization. Building on these models by including more psychological variables and incorporating more detailed simulations could result in a useful method to predict future mass behaviors (Musse et al, 2021).…”

Section: Figurementioning

confidence: 99%

Madness of the crowd: Understanding mass behaviors through a multidisciplinary lens

et al. 2022

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Mass or crowd behaviors refer to those that occur at a group level and suggest that crowds behave differently to individuals. Mass behaviors are typically triggered by a significant societal event. The ongoing COVID-19 pandemic has provided many tangible examples of crowd behaviors that have been observed globally, suggesting possible common underlying drivers. It is important to provide a deeper understanding of such behaviors to develop mitigation strategies for future population-level challenges. To gain deeper insight into a variety of crowd behaviors, we perform a conceptual analysis of crowd behaviors using three detailed case studies covering observable behavior (panic buying and health protective actions) and mass beliefs (conspiracy theories) that have resulted or shifted throughout the pandemic. The aim of this review was to explored key triggers, psychological drivers, and possible mitigation strategies through a mixture of theory and published literature. Finally, we create experimental mathematical models to support each case study and to illustrate the effects of manipulating key behavioral factors. Overall, our analyses identified several commonalties across the case studies and revealed the importance of Social Identity Theory and concepts of trust, social connection, and stress.

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“…Crowd simulation has been widely studied in the field of computer graphics, where each agent carries out tasks in a dynamic environment with complex interaction among multiple moving or static agents. 1 One important capability of crowd simulation is the accurate state prediction of each agent in the near future through a series of decision cycles. Here the agent can be considered as an individual pedestrian, a moving car, a mobile robot and even an obstacle.…”

Section: Introductionmentioning

confidence: 99%

“…The blue one is generated by our model through adding the Gaussian kernel term. The length of the solid line is1 2 |u| .…”

mentioning

confidence: 99%

ORCANet: Differentiable multi‐parameter learning for crowd simulation

Zhang

Wang

et al. 2022

Computer Animation & Virtual

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Realistic crowd simulation has always been an important research field in computer graphics. While both agent-based motion models and data-driven behavior models have made some progress, they are still suffering from either huge effort of multi-parameter tuning or limited realistic motion. In this article, we propose a novel and differentiable multi-parameter learning method for crowd simulation, which is called ORCANet. The main idea is to learn from real data and inverse evaluating the multi-parameter for subsequent simulation.ORCANet uses classic optimal reciprocal collision avoidance (ORCA) as a basic motion model which is integrated into the deep learning framework. Addressing the feature of linear programming and non-differentiable operation, a Gaussian kernel is added to approximate the role of neighbor distance in collision avoidance, which turns the original discrete operation into a fully differentiable forward simulation. Furthermore, we leverage ORCANet to optimize the multi-parameter combination in synthetic and real-world datasets. ORCANet is proved to rapidly converge to correct parameter values and regenerate the input synthetic sequence. Moreover, experiments on real-world datasets by the metric of pedestrian trajectories verified that a more realistic crowd simulation has been generated through ORCANet.

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A history of crowd simulation: the past, evolution, and new perspectives

Cited by 16 publications

References 109 publications

How do we perceive Characters? An Analysis of Human Perception in Still Images, Animations and VR Scenarios

How do we perceive Characters? An Analysis of Human Perception in Still Images, Animations and VR Scenarios

Madness of the crowd: Understanding mass behaviors through a multidisciplinary lens

ORCANet: Differentiable multi‐parameter learning for crowd simulation

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