用未来轨迹预测学习足球运动表示，捕捉动作不确定性。
Capturing Uncertainty in Human Motion for Representation Learning in Soccer
- 通过概率分布建模未来动作的多种可能路径。
- 在大规模足球追踪数据上提升预测准确率。
- 表示能力可迁移至多类下游任务，适合动作分析研究者。
本文提出一种基于自监督的学习框架，用于理解足球场景中的3D骨骼动作，以未来运动预测为学习目标。由于人类动作具有固有的不确定性，需考虑多个合理的未来轨迹才能准确捕捉运动动态并学习有效表示。为此，我们引入一个条件模块，对三维欧几里得空间中的离散化未来动作建模概率分布，并通过未来轨迹的显式监督学习多模态特性。在大规模足球运动员追踪数据上的实验表明，该方法显著提升了运动预测精度。此外，所学表示能有效迁移到多个足球下游任务中，展现出强大的跨任务泛化能力。
原文摘要 · Abstract (English)
This paper presents a self-supervised representation learning framework for understanding 3D skeleton-based human motion in soccer, using future motion prediction as the learning objective. Since human motion is inherently uncertain, accounting for multiple plausible futures is essential for capturing the underlying motion dynamics and learning effective representations. To this end, we introduce a conditioning module for motion prediction that models a probabilistic distribution over discretized future motions in 3D Euclidean space, learning multimodality with explicit supervision from future trajectories. Experiments on large-scale soccer player tracking data show that our approach substantially improves motion prediction accuracy. Moreover, the learned representations effectively transfer to multiple soccer downstream applications, demonstrating strong cross-task generalization.
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