GENESIS: generation of E-population based on statistical information

Seo, Hyewon; Yahia-Cherif, L.; Goto, Taro; Magnenat‐Thalmann, Nadia

doi:10.1109/ca.2002.1017511

Cited by 3 publications

(1 citation statement)

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“…Classic research about crowd simulation mainly focuses on the efficiency of the free crowd to reach the destination [9]- [11], in various scenarios such as sparse or dense crowds. Most of them divided the process of crowd simulation into two parts, global path planning and local obstacle avoiding [12]- [14], which is regardless of the artistic effect of crowd transform process. However, the crowd formation transformation in the performance focuses more on the aesthetic effect created by formation transform.…”

Section: Related Workmentioning

confidence: 99%

A Spatio-Temporal Hierarchical Model for Crowd Formation Planning in Large-Scale Performance

Huang

Liu

et al. 2020

IEEE Access

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Crowd formation of aesthetic transformation is considered to have extremely high artistic value and is widely applied in large-scale performances. In this paper, a spatio-temporal hierarchical model that parts the crowd formation transform into multiple granularities is proposed. Its core idea is to add spatio-temporal constraints created by directors into transformation process after multi-level division. In this model, average hash value and energy optimization are used to achieve reasonable crowd formation arrangement, while smooth and collision-free formation transformations are presented by constrained region growth and Kuhn-Munkres algorithm. We have also proposed a framework to achieve the generation of visually pleasing crowd formation transform performance based on the constraints. Besides, a virtual crowd formation transformation simulation was built to verify the effect of the proposed model. Through simulation experiments and comparisons, it was demonstrated that this hierarchical model can generate aesthetic crowd formation transformation with a satisfactory process.

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Section: Related Workmentioning

confidence: 99%