Publication
Unified uncertainty-aware diffusion for multi-agent trajectory modeling
Conference Article
Conference
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Edition
2025
Pages
22476-22486
File
Abstract
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.
Categories
pattern recognition.
Author keywords
Soccer, Uncertainty, Diffusion models
Scientific reference
G. Capellera, A. Rubio, L. Ferraz and A. Agudo. Unified uncertainty-aware diffusion for multi-agent trajectory modeling, 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025, Nashville, TN (USA), pp. 22476-22486, to appear.
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