PATS: Proficiency-Aware Temporal Sampling for Multi-View Sports Skill Assessment

TL;DR

PATS is a novel temporal sampling method that preserves complete movement segments in videos, improving multi-view sports skill assessment accuracy across various activities by adaptively segmenting videos based on activity characteristics.

Contribution

This paper introduces PATS, an adaptive temporal sampling strategy that maintains full fundamental movements within segments, enhancing automated sports skill assessment accuracy.

Findings

PATS outperforms state-of-the-art methods on the EgoExo4D benchmark.

PATS achieves significant accuracy gains in challenging domains like bouldering and music.

PATS adapts effectively to diverse activity dynamics, from high-frequency to fine-grained segmentation.

Abstract

Automated sports skill assessment requires capturing fundamental movement patterns that distinguish expert from novice performance, yet current video sampling methods disrupt the temporal continuity essential for proficiency evaluation. To this end, we introduce Proficiency-Aware Temporal Sampling (PATS), a novel sampling strategy that preserves complete fundamental movements within continuous temporal segments for multi-view skill assessment. PATS adaptively segments videos to ensure each analyzed portion contains full execution of critical performance components, repeating this process across multiple segments to maximize information coverage while maintaining temporal coherence. Evaluated on the EgoExo4D benchmark with SkillFormer, PATS surpasses the state-of-the-art accuracy across all viewing configurations (+0.65% to +3.05%) and delivers substantial gains in challenging domains (+26.22% bouldering, +2.39% music, +1.13% basketball). Systematic analysis reveals that PATS successfully adapts to diverse activity characteristics-from high-frequency sampling for dynamic sports to fine-grained segmentation for sequential skills-demonstrating its effectiveness as an adaptive approach to temporal sampling that advances automated skill assessment for real-world applications. Visit our project page at https://edowhite.github.io/PATS