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Guillem Capellera

2 accepted papers

2026

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

ICLR 2026poster

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 $\textbf{JointDiff}$, a novel diffusion framework designed to unify these two processes by simultane…

Cited by 0SourcecodeScholar
2025

Unified Uncertainty-Aware Diffusion for Multi-Agent Trajectory Modeling

CVPR 2025poster

Multi-agent trajectory modeling has primarily focused on forecasting future states, often overlooking broader tasks like trajectory completion, which are crucial for real-world applications such as correcting tracking data. Existing methods also generally predict agents' states without offering any…