Capturing Uncertainty in Human Motion for Representation Learning in Soccer

arXiv:2608.11203v1 Announce Type: new Abstract: 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.

Skeleton-Snippet Contrastive Learning with Multiscale Feature Fusion for Action Localization
Exploring Adaptive Masked Reconstruction for Self-Supervised Skeleton-Based Action Recognition
Beyond Binary Contrast: Modeling Continuous Skeleton Action Spaces with Transitional Anchors
T-MOR: Learning Motion-Aware Skeleton Representations for Human Action Recognition

Source: arXiv cs.CV | 2026-08-12

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