标题： 虚拟击剑者：基于从真实视频中提取的策略生成击剑比赛 显示英文标题

标题： VirtualFencer: Generating Fencing Bouts based on Strategies Extracted from In-the-Wild Videos

摘要： 击剑是一项运动员进行多种但具有战略逻辑动作的运动。 虽然大多数动作属于几个高层次的动作（例如，步法、突刺、格挡），但执行方式可以有很大差异——快与慢，大与小，进攻与防守。 此外，击剑选手的动作受到策略的影响，这种策略通常是对对手行为的回应。 这种动作多样性与底层双人策略的结合促使将数据驱动建模应用于击剑。 我们提出了VirtualFencer，一个能够从野外视频中无监督地提取3D击剑动作和策略的系统，然后利用提取的知识生成逼真的击剑行为。 我们通过让系统（i）与自己对战（自对弈），（ii）与在线视频中的真实击剑选手动作对战，以及（iii）与专业击剑选手进行交互式对战，展示了系统的多功能能力。 显示英文摘要

摘要： 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.