发表机构
KTH Royal Institute of Technology; EA Sports TRACAB（皇家理工学院; EA Sports TRACAB）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种用于足球3D骨骼动作的自监督表示学习框架，通过建模未来动作概率分布提升预测准确率，该表示可迁移至多个足球下游应用，展现强跨任务泛化能力。
AI 中文摘要
本文提出一种用于理解足球中基于3D骨骼的人类动作的自监督表示学习框架，采用未来动作预测作为学习目标。由于人类动作固有不确定性，考虑多种合理未来动作对捕捉潜在动作动态并学习有效表示至关重要。为此，我们引入一种用于动作预测的条件模块，该模块在3D欧几里得空间中对离散未来动作的概率分布进行建模，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跟踪数据上的实验表明，我们的方法大幅提升了动作预测准确率。此外，学习到的表示可有效迁移至多个足球下游应用，展现出强大的跨任务泛化能力。
英文摘要
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.
