提出CausalTraj模型，让团队运动中多球员轨迹预测更连贯真实。
Coherent Multi-Agent Trajectory Forecasting in Team Sports with CausalTraj
- 基于因果时序建模，生成联合合理的多主体轨迹
- 在三组数据集上达成最佳联合预测精度
- 适合需要真实团队互动模拟的体育分析场景
联合预测多个相互作用主体的轨迹是体育分析等复杂群体动态领域中的核心挑战。准确预测可实现游戏进程的逼真模拟与策略理解。现有大多数模型仅以单主体精度指标（minADE、minFDE）评估，这些指标独立衡量每个主体的最佳预测结果，忽略了预测轨迹能否共同构成合理多主体未来。许多前沿模型主要基于此类指标设计优化，导致在联合预测上表现不佳，且难以生成可解释的团队运动情景。本文提出CausalTraj，一种时间因果、基于似然的模型，旨在生成联合可能的多主体轨迹预测。为更好评估集体建模能力，强调联合指标（minJADE、minJFDE），衡量最优生成情景中各主体的联合准确性。在NBA SportVU、Basketball-U和Football-U数据集上，CausalTraj在单主体精度上保持竞争力，并在联合指标上取得当前最优结果，同时生成定性上连贯、真实的比赛演变过程。
原文摘要 · Abstract (English)
Jointly forecasting trajectories of multiple interacting agents is a core challenge in sports analytics and other domains involving complex group dynamics. Accurate prediction enables realistic simulation and strategic understanding of gameplay evolution. Most existing models are evaluated solely on per-agent accuracy metrics (minADE, minFDE), which assess each agent independently on its best-of-k prediction. However these metrics overlook whether the model learns which predicted trajectories can jointly form a plausible multi-agent future. Many state-of-the-art models are designed and optimized primarily based on these metrics. As a result, they may underperform on joint predictions and also fail to generate coherent, interpretable multi-agent scenarios in team sports. We propose CausalTraj, a temporally causal, likelihood-based model that is built to generate jointly probable multi-agent trajectory forecasts. To better assess collective modeling capability, we emphasize joint metrics (minJADE, minJFDE) that measure joint accuracy across agents within the best generated scenario sample. Evaluated on the NBA SportVU, Basketball-U, and Football-U datasets, CausalTraj achieves competitive per-agent accuracy and the best recorded results on joint metrics, while yielding qualitatively coherent and realistic gameplay evolutions.
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