Gauging an individual's skill level is crucial, as it inherently shapes their behavior. Quantifying skill, however, is challenging because it is latent to the observed actions. To explore skill understanding in human behavior, we focus on dyadic sports -- specifically table tennis -- where skill manifests not just in complex movements, but in the subtle nuances of execution conditioned on game context. Our key idea is to learn a generative model of each player's tactical racket strokes and jointly embed them in a common latent space that encodes individual characteristics, including those pertaining to skill levels. By training these player models on a large-scale dataset of 3D-reconstructed professional matches and conditioning them on comprehensive game context -- including player positioning and opponent behaviors -- the models capture individual tactical identities within their latent space. We probe this learned player space and find that it reflects distinct play styles and attributes that collectively represent skill. By training a simple relative ranking network on these embeddings, we demonstrate that both relative and absolute skill predictions can be achieved. These results demonstrate that the learned player space effectively quantifies skill levels, providing a foundation for automated skill assessment in complex, interactive behaviors.

翻译：评估个体技能水平至关重要，因为它从根本上塑造了个人的行为方式。然而，量化技能存在挑战，因为技能隐含于可观测行为之中。为探究人类行为中的技能理解，我们聚焦于双人对抗性运动——特别是乒乓球——在该运动中，技能不仅体现在复杂动作上，更体现在根据比赛情境调整的细微执行差异中。我们的核心思路是学习每位球员战术性击球动作的生成模型，并将其共同嵌入一个统一的潜在空间，该空间可编码个体特征（包括与技术水平相关的特征）。通过基于大规模三维重建职业比赛数据集训练这些球员模型，并以全面的比赛情境（包括球员站位与对手行为）为条件，模型可在其潜在空间中捕捉个体战术特征。我们探究这一习得的球员空间，发现它反映了共同构成技能的不同打法风格与属性。基于这些嵌入训练一个简单的相对排序网络后，我们证明既可实现相对技能预测，也能实现绝对技能预测。这些结果表明，习得的球员空间能有效量化技术水平，为复杂互动行为中的自动化技能评估奠定了基础。