Text-to-Video Generative Models for Simulation-Based Intelligent Training and Scenario Generation
Keywords:
text-to-video generation, simulation-based training, scenario generation, generative AI, diffusion models, digital twins, intelligent training systems, socio-technical infrastructure, policy and governanceAbstract
The emergence of text-to-video generative models represents a transformative advancement in the synthesis of dynamic visual content from natural language descriptions, with profound implications for simulation-based intelligent training and scenario generation. This paper presents a comprehensive systems-level analysis of these models as foundational components within large-scale socio-technical infrastructures for training, education, and decision support. We examine architectural trade-offs among autoregressive transformers, diffusion models, and hybrid frameworks, focusing on their capacity to produce temporally coherent, physically plausible, and procedurally controllable video sequences. The paper further explores the integration of these generative models with existing simulation engines, reinforcement learning environments, and digital twin ecosystems, highlighting structural challenges related to real-time inference, data governance, computational sustainability, and robustness. A comparative analysis across defense, healthcare, autonomous driving, and disaster response domains illustrates how deployment context shapes model design, scenario diversity, and evaluation metrics. Critical considerations of fairness, bias propagation, and policy implications are discussed, emphasizing the need for transparent auditing mechanisms and human-in-the-loop validation. By synthesizing recent advances in generative AI with simulation science, this paper offers a forward-looking perspective on the governance and infrastructural requirements for deploying text-to-video models in high-stakes training applications, ultimately arguing that their responsible integration depends on harmonizing technical capability with institutional accountability.
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