Understanding multi-agent behavior is critical across various fields. The
conventional approach involves analyzing agent movements through three primary
tasks: trajectory prediction, imputation, and spatial-temporal recovery.
Considering the unique input formulation and constraint of these tasks, most
existing methods are tailored to address only one specific task. However, in
real-world applications, these scenarios frequently occur simultaneously.
Consequently, methods designed for one task often fail to adapt to others,
resulting in performance drops. To overcome this limitation, we propose a
Unified Trajectory Generation model, UniTraj, that processes arbitrary
trajectories as masked inputs, adaptable to diverse scenarios. Specifically, we
introduce a Ghost Spatial Masking (GSM) module embedded within a Transformer
encoder for spatial feature extraction. We further extend recent successful
State Space Models (SSMs), particularly the Mamba model, into a Bidirectional
Temporal Mamba to effectively capture temporal dependencies. Additionally, we
incorporate a Bidirectional Temporal Scaled (BTS) module to comprehensively
scan trajectories while maintaining the temporal missing relationships within
the sequence. We curate and benchmark three practical sports game datasets,
Basketball-U, Football-U, and Soccer-U, for evaluation. Extensive experiments
demonstrate the superior performance of our model. To the best of our
knowledge, this is the first work that addresses this unified problem through a
versatile generative framework, thereby enhancing our understanding of
multi-agent movement. Our datasets, code, and model weights are available at
this https URL

通过引入嵌入于 Transformer 编码器中的 Ghost Spatial Masking 模块进行空间特征提取，将 State Space Models (SSMs) 扩展为双向时间 Mamba 以捕捉时间依赖性，以及结合双向时间比例变换 (Bidirectional Temporal Scaled) 模块来全面扫描轨迹，本研究通过多功能生成框架首次解决了这个统一问题，增进了我们对多智能体运动的理解。