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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.20117 |
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| _version_ | 1866909825111162880 |
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| author | Liu, Xiaolin Zhou, Tianyi Kang, Hongbo Ma, Jian Wang, Ziwen Huang, Jing Weng, Wenguo Lai, Yu-Kun Li, Kun |
| author_facet | Liu, Xiaolin Zhou, Tianyi Kang, Hongbo Ma, Jian Wang, Ziwen Huang, Jing Weng, Wenguo Lai, Yu-Kun Li, Kun |
| contents | Crowd evacuation simulation is critical for enhancing public safety, and demanded for realistic virtual environments. Current mainstream evacuation models overlook the complex human behaviors that occur during evacuation, such as pedestrian collisions, interpersonal interactions, and variations in behavior influenced by terrain types or individual body shapes. This results in the failure to accurately simulate the escape of people in the real world. In this paper, aligned with the sensory-decision-motor (SDM) flow of the human brain, we propose a real-time 3D crowd evacuation simulation framework that integrates a 3D-adaptive SFM (Social Force Model) Decision Mechanism and a Personalized Gait Control Motor. This framework allows multiple agents to move in parallel and is suitable for various scenarios, with dynamic crowd awareness. Additionally, we introduce Part-level Force Visualization to assist in evacuation analysis. Experimental results demonstrate that our framework supports dynamic trajectory planning and personalized behavior for each agent throughout the evacuation process, and is compatible with uneven terrain. Visually, our method generates evacuation results that are more realistic and plausible, providing enhanced insights for crowd simulation. The code is available at http://cic.tju.edu.cn/faculty/likun/projects/RESCUE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20117 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RESCUE: Crowd Evacuation Simulation via Controlling SDM-United Characters Liu, Xiaolin Zhou, Tianyi Kang, Hongbo Ma, Jian Wang, Ziwen Huang, Jing Weng, Wenguo Lai, Yu-Kun Li, Kun Computer Vision and Pattern Recognition Crowd evacuation simulation is critical for enhancing public safety, and demanded for realistic virtual environments. Current mainstream evacuation models overlook the complex human behaviors that occur during evacuation, such as pedestrian collisions, interpersonal interactions, and variations in behavior influenced by terrain types or individual body shapes. This results in the failure to accurately simulate the escape of people in the real world. In this paper, aligned with the sensory-decision-motor (SDM) flow of the human brain, we propose a real-time 3D crowd evacuation simulation framework that integrates a 3D-adaptive SFM (Social Force Model) Decision Mechanism and a Personalized Gait Control Motor. This framework allows multiple agents to move in parallel and is suitable for various scenarios, with dynamic crowd awareness. Additionally, we introduce Part-level Force Visualization to assist in evacuation analysis. Experimental results demonstrate that our framework supports dynamic trajectory planning and personalized behavior for each agent throughout the evacuation process, and is compatible with uneven terrain. Visually, our method generates evacuation results that are more realistic and plausible, providing enhanced insights for crowd simulation. The code is available at http://cic.tju.edu.cn/faculty/likun/projects/RESCUE. |
| title | RESCUE: Crowd Evacuation Simulation via Controlling SDM-United Characters |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.20117 |