PATS：面向多视角运动技能评估的熟练度感知时序采样

摘要

自动化运动技能评估需要捕捉区分专家与新手表现的基本动作模式，然而当前的视频采样方法破坏了评估熟练度所必需的时间连续性。为此，我们提出了熟练度感知时间采样（PATS），这是一种新颖的采样策略，能够在连续的时间段内保留完整的基本动作，以支持多视角技能评估。PATS自适应地分割视频，确保每个分析部分都包含关键表现组件的完整执行，并在多个片段中重复此过程，以在保持时间连贯性的同时最大化信息覆盖。在EgoExo4D基准测试中，结合SkillFormer进行评估，PATS在所有视角配置下均超越了现有技术的准确率（提升幅度从+0.65%到+3.05%），并在挑战性领域取得了显著进步（攀岩+26.22%，音乐+2.39%，篮球+1.13%）。系统分析表明，PATS能够成功适应多样化的活动特征——从针对动态运动的高频采样到针对序列技能的精细分割——证明了其作为一种自适应时间采样方法在推进现实世界应用中的自动化技能评估方面的有效性。

English

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.

PATS：面向多视角运动技能评估的熟练度感知时序采样

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

摘要

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