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arXiv · 2509.22522

JointDiff: Bridging Continuous and Discrete in Multi-Agent Trajectory Generation

Guillem Capellera·Luis Ferraz·Antonio Rubio·Alexandre Alahi·Antonio Agudo
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Abstract

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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Guillem Capellera, Luis Ferraz, Antonio Rubio, Alexandre Alahi, Antonio Agudo. 2025-09-26. JointDiff: Bridging Continuous and Discrete in Multi-Agent Trajectory Generation. https://arxiv.org/abs/2509.22522

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