统一生成连续轨迹与同步离散事件，提升多智能体系统建模真实性。
JointDiff: Bridging Continuous and Discrete in Multi-Agent Trajectory Generation
- 提出联合扩散框架，同步生成位置轨迹与球权等离散事件。
- 在足球数据集上达到当前最佳性能，支持灵活语义控制。
- 适合需要可控、真实交互模拟的研究者与开发者。
生成模型通常将连续数据与离散事件视为独立过程，导致复杂系统中二者交互建模的缺失。为此，我们提出JointDiff，一种新型扩散框架，可同时生成连续时空数据与同步发生的离散事件。我们在体育领域验证其有效性，实现多智能体轨迹与关键球权事件的联合建模。该方法通过不可控生成及两种新可控生成场景进行验证：弱持球人引导（仅需指定预期持球方列表）与文本引导（通过自然语言精细控制比赛动态）。为支持这些引导信号，我们引入跨引导机制CrossGuid。此外，我们发布了包含文本描述的统一体育基准数据集（涵盖足球与美式橄榄球）。JointDiff在多个指标上达到领先水平，证明联合建模对构建真实且可控的交互系统生成模型至关重要。
原文摘要 · Abstract (English)
Generative models often treat continuous data and discrete events as separate processes, creating a gap in modeling complex systems where they interact synchronously. To bridge this gap, we introduce JointDiff, a novel diffusion framework designed to unify these two processes by simultaneously generating continuous spatio-temporal data and synchronous discrete events. We demonstrate its efficacy in the sports domain by simultaneously modeling multi-agent trajectories and key possession events. This joint modeling is validated with non-controllable generation and two novel controllable generation scenarios: weak-possessor-guidance, which offers flexible semantic control over game dynamics through a simple list of intended ball possessors, and text-guidance, which enables fine-grained, language-driven generation. To enable the conditioning with these guidance signals, we introduce CrossGuid, an effective conditioning operation for multi-agent domains. We also share a new unified sports benchmark enhanced with textual descriptions for soccer and football datasets. JointDiff achieves state-of-the-art performance, demonstrating that joint modeling is crucial for building realistic and controllable generative models for interactive systems. https://guillem-cf.github.io/JointDiff/
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