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

翻译：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运动模式，然而当前的视频采样方法破坏了熟练度评估所必需的时间连续性。为此，我们提出了熟练度感知时序采样（PATS），这是一种新颖的采样策略，可在连续时间片段内保留完整的基本运动，用于多视角技能评估。PATS自适应地对视频进行分割，以确保每个分析部分都包含关键表现成分的完整执行过程，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范围。在EgoExo4D基准上使用SkillFormer进行评估，PATS在所有视角配置下的准确率均超越了现有最佳水平（+0.65% 至 +3.05%），并在具有挑战性的领域取得了显著提升（攀岩+26.22%，音乐+2.39%，篮球+1.13%）。系统分析表明，PATS成功适应了多样化的活动特性——从动态运动的高频采样到顺序技能的细粒度分割——证明了其作为一种自适应时序采样方法的有效性，推动了面向实际应用的自动化技能评估的发展。请访问我们的项目页面：https://edowhite.github.io/PATS