Fencing is a sport where athletes engage in diverse yet strategically logical motions. While most motions fall into a few high-level actions (e.g. step, lunge, parry), the execution can vary widely-fast vs. slow, large vs. small, offensive vs. defensive. Moreover, a fencer's actions are informed by a strategy that often comes in response to the opponent's behavior. This combination of motion diversity with underlying two-player strategy motivates the application of data-driven modeling to fencing. We present VirtualFencer, a system capable of extracting 3D fencing motion and strategy from in-the-wild video without supervision, and then using that extracted knowledge to generate realistic fencing behavior. We demonstrate the versatile capabilities of our system by having it (i) fence against itself (self-play), (ii) fence against a real fencer's motion from online video, and (iii) fence interactively against a professional fencer.

翻译：击剑是一项运动员进行多样化但策略性逻辑动作的运动。虽然大多数动作可归为少数几种高级动作（如步法、弓步、格挡），但其执行方式却千差万快慢、大小、攻防各异。此外，击剑运动员的动作往往基于针对对手行为制定的策略。这种动作多样性与双人底层策略的结合，促使我们将数据驱动建模应用于击剑领域。本文提出VirtualFencer系统，该系统能够从无监督的真实视频中提取三维击剑动作与策略，并利用提取的知识生成逼真的击剑行为。我们通过以下实验展示系统的多功能性：（i）系统自我对弈，（ii）与在线视频中真实击剑运动员的动作对抗，以及（iii）与职业击剑运动员进行交互式对抗。