Autonomous Agent Crowd Simulation in Immersive Virtual Reality

In the last decade, the simulation of credible and convincing crowd behavior has become increasingly important across a wide range of domains, including video games, social networks and the Metaverse, cinematic computergenerated imagery, urban planning, evacuation and riot simulations, military training, and cultural heritage preservation. This research aims to design techniques to simulate realistic crowds of autonomous agents in an efficient and scalable way, with particular attention to immersive Virtual Reality applications. The research focuses on increasing the variety and plausibility of agents relying on psychological metrics and profiles, appearance, individual behaviors, and cultural aspects without neglecting simulation performance and optimization. Key aspects of my research include modeling personality traits, emotional states, and their influence on agents’ clothing and facial expressions to foster richer individualization. In parallel, my research will explore algorithms for resource-efficient, fast, and scalable crowd simulation. In particular, the research will employ data-driven methods, such as Machine Learning and Deep Learning, to support behavior modeling and the adaptability of agents through context understanding. The proposed approaches will be validated through applied scenarios, including user testing in serious games and training applications across various domains.