Understanding trajectories in multi-agent scenarios requires addressing various tasks, including predicting future movements, imputing missing observations, inferring the status of unseen agents, and classifying different global states. Traditional data-driven approaches often handle these tasks separately with specialized models. We introduce TranSPORTmer, a unified transformer-based framework capable of addressing all these tasks, showcasing its application to the intricate dynamics of multi-agent sports scenarios like soccer and basketball. Using Set Attention Blocks, TranSPORTmer effectively captures temporal dynamics and social interactions in an equivariant manner. The model's tasks are guided by an input mask that conceals missing or yet-to-be-predicted observations. Additionally, we introduce a CLS extra agent to classify states along soccer trajectories, including passes, possessions, uncontrolled states, and out-of-play intervals, contributing to an enhancement in modeling trajectories. Evaluations on soccer and basketball datasets show that TranSPORTmer outperforms state-of-the-art task-specific models in player forecasting, player forecasting-imputation, ball inference, and ball imputation. https://youtu.be/8VtSRm8oGoE

翻译：理解多智能体场景中的轨迹需要处理多种任务，包括预测未来运动、补全缺失观测、推断未观测智能体的状态以及分类不同的全局状态。传统的数据驱动方法通常使用专门模型分别处理这些任务。我们提出了TranSPORTmer，一个基于Transformer的统一框架，能够处理所有上述任务，并展示了其在足球和篮球等多智能体体育复杂动态场景中的应用。通过使用集合注意力模块，TranSPORTmer以等变方式有效捕捉时间动态和社会交互。模型的任务由一个输入掩码引导，该掩码用于隐藏缺失或待预测的观测值。此外，我们引入了一个CLS额外智能体，用于对足球轨迹中的状态进行分类，包括传球、控球、非控球状态和出界时段，从而有助于提升轨迹建模能力。在足球和篮球数据集上的评估表明，TranSPORTmer在球员预测、球员预测-补全、球体推断和球体补全任务上均优于当前最先进的专用模型。https://youtu.be/8VtSRm8oGoE