FASTSWARM: A Data-driven FrAmework for Real-time Flying InSecT SWARM Simulation
Wei Xiang, Xinran Yao, He Wang, Xiaogang Jin

TL;DR
FASTSWARM is a data-driven, real-time framework for simulating realistic insect swarms with high fidelity and scalability, based on real-world data and energy minimization techniques.
Contribution
It introduces a novel, scalable, data-driven approach for realistic insect swarm simulation that explicitly models complex behaviors and achieves real-time performance.
Findings
Achieves real-time simulation of large insect swarms
Provides high-fidelity behavior modeling based on real data
Demonstrates versatility and controllability in various swarm scenarios
Abstract
Insect swarms are common phenomena in nature and therefore have been actively pursued in computer animation. Realistic insect swarm simulation is difficult due to two challenges: high-fidelity behaviors and large scales, which make the simulation practice subject to laborious manual work and excessive trial-and-error processes. To address both challenges, we present a novel data-driven framework, FASTSWARM, to model complex behaviors of flying insects based on real-world data and simulate plausible animations of flying insect swarms. FASTSWARM has a linear time complexity and achieves real-time performance for large swarms. The high-fidelity behavior model of FASTSWARM explicitly takes into consideration the most common behaviors of flying insects, including the interactions among insects such as repulsion and attraction, the self-propelled behaviors such as target following and…
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Taxonomy
TopicsEvacuation and Crowd Dynamics · Human Motion and Animation · Music Technology and Sound Studies
