Effective modeling of group interactions and dynamic semantic intentions is crucial for forecasting behaviors like trajectories or movements. In complex scenarios like sports, agents' trajectories are influenced by group interactions and intentions, including team strategies and opponent actions. To this end, we propose a novel diffusion-based trajectory prediction framework that integrates group-level interactions into a conditional diffusion model, enabling the generation of diverse trajectories aligned with specific group activity. To capture dynamic semantic intentions, we frame group interaction prediction as a cooperative game, using Banzhaf interaction to model cooperation trends. We then fuse semantic intentions with enhanced agent embeddings, which are refined through both global and local aggregation. Furthermore, we expand the NBA SportVU dataset by adding human annotations of team-level tactics for trajectory and tactic prediction tasks. Extensive experiments on three widely-adopted datasets demonstrate that our model outperforms state-of-the-art methods. Our source code and data are available at https://github.com/aurora-xin/Group2Int-trajectory.

翻译：有效建模群体交互与动态语义意图对于预测轨迹或运动等行为至关重要。在体育等复杂场景中，智能体的轨迹受到群体交互与意图的影响，包括团队策略与对手行为。为此，我们提出一种新颖的基于扩散的轨迹预测框架，将群体层面的交互整合到条件扩散模型中，从而能够生成符合特定群体活动的多样化轨迹。为捕捉动态语义意图，我们将群体交互预测构建为合作博弈，利用Banzhaf交互建模合作趋势。随后，我们将语义意图与增强的智能体嵌入相融合，这些嵌入通过全局与局部聚合进行优化。此外，我们扩展了NBA SportVU数据集，为其增加了团队层面战术的人工标注，以支持轨迹与战术预测任务。在三个广泛采用的数据集上进行的大量实验表明，我们的模型性能优于现有最先进方法。我们的源代码与数据可在 https://github.com/aurora-xin/Group2Int-trajectory 获取。