标题： Gen-C：用生成人群填充虚拟世界 显示英文标题

标题： Gen-C: Populating Virtual Worlds with Generative Crowds

摘要： 在过去二十年中，研究人员在模拟人类人群方面取得了显著进展，但这些努力主要集中在低级任务，如避障和路径跟随和 flocking 等有限的行为范围上。 然而，创建引人入胜的人群场景不仅仅需要功能性的移动——还需要捕捉代理之间、代理与环境之间的高层互动，并随时间变化。 为了解决这个问题，我们引入了 Gen-C，这是一种生成模型，用于自动化编写高层人群行为的任务。 Gen-C 通过利用大型语言模型（LLM）生成有限的一组人群场景，从而绕过了收集和标注真实人群视频数据这一劳动密集且具有挑战性的任务，这些场景随后通过模拟扩展和泛化，构建出时间扩展的图，以建模虚拟代理的动作和交互。 我们的方法采用两个由条件先验网络引导的变分图自动编码器：一个专门用于学习图结构（代理交互）的潜在空间，另一个用于节点特征（代理动作和导航）。 这种设置使得动态人群交互的灵活生成成为可能。 训练好的模型可以基于自然语言进行条件设置，使用户能够从文本描述中合成新的群体行为。 我们在两个场景中展示了我们方法的有效性，即大学校园和火车站，展示了其在用表现出多样化和动态行为的代理填充各种虚拟环境方面的潜力，这些行为反映了复杂的互动和高层决策模式。 显示英文摘要

摘要： Over the past two decades, researchers have made significant advancements in simulating human crowds, yet these efforts largely focus on low-level tasks like collision avoidance and a narrow range of behaviors such as path following and flocking. However, creating compelling crowd scenes demands more than just functional movement-it requires capturing high-level interactions between agents, their environment, and each other over time. To address this issue, we introduce Gen-C, a generative model to automate the task of authoring high-level crowd behaviors. Gen-C bypasses the labor-intensive and challenging task of collecting and annotating real crowd video data by leveraging a large language model (LLM) to generate a limited set of crowd scenarios, which are subsequently expanded and generalized through simulations to construct time-expanded graphs that model the actions and interactions of virtual agents. Our method employs two Variational Graph Auto-Encoders guided by a condition prior network: one dedicated to learning a latent space for graph structures (agent interactions) and the other for node features (agent actions and navigation). This setup enables the flexible generation of dynamic crowd interactions. The trained model can be conditioned on natural language, empowering users to synthesize novel crowd behaviors from text descriptions. We demonstrate the effectiveness of our approach in two scenarios, a University Campus and a Train Station, showcasing its potential for populating diverse virtual environments with agents exhibiting varied and dynamic behaviors that reflect complex interactions and high-level decision-making patterns.