用扩散模型预测多人运动轨迹，融合团队策略与协作关系。
Learning Group Interactions and Semantic Intentions for Multi-Object Trajectory Prediction
- 基于扩散模型，融合群体互动与动态意图建模
- 在三个数据集上轨迹预测误差低于现有方法
- 适合体育分析、智能导航等需理解群体行为的场景
有效建模群体互动与动态语义意图对预测复杂行为（如轨迹）至关重要。在体育等复杂场景中，个体轨迹受团队策略与对手动作影响。本文提出一种新型基于扩散的轨迹预测框架，将群体层级互动引入条件扩散模型，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，将群体互动预测建模为合作博弈，采用Banzhaf交互指数刻画合作趋势，并融合语义意图增强代理嵌入，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，加入人工标注的团队战术标签，用于轨迹与战术预测任务。在三个常用数据集上的大量实验表明，本模型优于当前最优方法。代码与数据已公开于https://github.com/aurora-xin/Group2Int-trajectory。
原文摘要 · Abstract (English)
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.
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