Unified Uncertainty-Aware Diffusion for Multi-Agent Trajectory Modeling
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arXiv
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| Format: | Preprint |
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2025
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| author | Capellera, Guillem Rubio, Antonio Ferraz, Luis Agudo, Antonio |
| author_facet | Capellera, Guillem Rubio, Antonio Ferraz, Luis Agudo, Antonio |
| contents | 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 state-wise measure of uncertainty. Moreover, popular multi-modal sampling methods lack any error probability estimates for each generated scene under the same prior observations, making it difficult to rank the predictions during inference time. We introduce U2Diff, a \textbf{unified} diffusion model designed to handle trajectory completion while providing state-wise \textbf{uncertainty} estimates jointly. This uncertainty estimation is achieved by augmenting the simple denoising loss with the negative log-likelihood of the predicted noise and propagating latent space uncertainty to the real state space. Additionally, we incorporate a Rank Neural Network in post-processing to enable \textbf{error probability} estimation for each generated mode, demonstrating a strong correlation with the error relative to ground truth. Our method outperforms the state-of-the-art solutions in trajectory completion and forecasting across four challenging sports datasets (NBA, Basketball-U, Football-U, Soccer-U), highlighting the effectiveness of uncertainty and error probability estimation. Video at https://youtu.be/ngw4D4eJToE |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18589 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unified Uncertainty-Aware Diffusion for Multi-Agent Trajectory Modeling Capellera, Guillem Rubio, Antonio Ferraz, Luis Agudo, Antonio Computer Vision and Pattern Recognition 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 state-wise measure of uncertainty. Moreover, popular multi-modal sampling methods lack any error probability estimates for each generated scene under the same prior observations, making it difficult to rank the predictions during inference time. We introduce U2Diff, a \textbf{unified} diffusion model designed to handle trajectory completion while providing state-wise \textbf{uncertainty} estimates jointly. This uncertainty estimation is achieved by augmenting the simple denoising loss with the negative log-likelihood of the predicted noise and propagating latent space uncertainty to the real state space. Additionally, we incorporate a Rank Neural Network in post-processing to enable \textbf{error probability} estimation for each generated mode, demonstrating a strong correlation with the error relative to ground truth. Our method outperforms the state-of-the-art solutions in trajectory completion and forecasting across four challenging sports datasets (NBA, Basketball-U, Football-U, Soccer-U), highlighting the effectiveness of uncertainty and error probability estimation. Video at https://youtu.be/ngw4D4eJToE |
| title | Unified Uncertainty-Aware Diffusion for Multi-Agent Trajectory Modeling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.18589 |