Unified Uncertainty-Aware Diffusion for Multi-Agent Trajectory Modeling
Guillem Capellera, Antonio Rubio, Luis Ferraz, Antonio Agudo
2025Year
3Top-tier citations
Abstract
Figure 1 . Uncertainty-aware, unified and interpretable approach for trajectory modeling in multi-agent scenarios. U2Diff is a diffusion-based model capable of performing trajectory completion tasks such as forecasting, imputation or inferring totally unseen agents, while also jointly estimating state-wise uncertainty. RankNN is a post-processing operation that infers an error probability for each generated mode under the same prior observations, which is strongly correlated with the error related to the ground truth.
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Cited by top-tier papers3
- JointDiff: Bridging Continuous and Discrete in Multi-Agent Trajectory GenerationGuillem Capellera, Luis Ferraz, Antonio Romano, Alexandre Alahi et al.ICLR 2026 · 6 citations
- W2W: Language-Model-Based Trajectory Prediction with Reinforcement LearningZirui Xu, Biao Yang, Rongrong Ni, Zhongkai Zhou et al.CVPR 2026 · 2 citations
- PathCRF: Ball-Free Soccer Event Detection via Possession Path Inference from Player TrajectoriesHyunsung Kim, Kunhee Lee, Sangwoo Seo, Sang-Ki Ko et al.KDD 2026
Builds on27
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 35,902 citations
- Denoising Diffusion Implicit ModelsJiaming Song, Chenlin Meng, Stefano ErmonICLR 2021 · 11,743 citations
- Improved Denoising Diffusion Probabilistic ModelsAlexander Quinn Nichol, Prafulla DhariwalICML 2021 · 5,234 citations
- CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series ImputationYusuke Tashiro, Jiaming Song, Yang Song, Stefano ErmonNeurIPS 2021 · 1,245 citations
- AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent ForecastingYe Yuan, Xinshuo Weng, Yanglan Ou, Kris KitaniICCV 2021 · 658 citations
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