从真实视频中自动学习击剑策略与动作，生成逼真对战场景。
VirtualFencer: Generating Fencing Bouts based on Strategies Extracted from In-the-Wild Videos
- 无监督提取真实视频中的3D击剑动作与双人策略
- 可自动生成对战、与真人动作对抗或实时交互
- 适用于体育训练模拟与智能对抗系统开发
击剑运动包含多样但具有战略逻辑的动作，如突刺、滑步、格挡等，其执行方式差异显著（快慢、大小、攻防）。运动员的行为常根据对手动作即时调整。这种动作多样性与双人策略的结合，为数据驱动建模提供了基础。本文提出VirtualFencer系统，无需标注即可从真实视频中提取3D击剑动作与策略，并利用该知识生成逼真的对战行为。我们展示了系统在三种场景下的能力：自我对战、与在线视频中真人动作对打、以及与专业选手实时互动。
原文摘要 · Abstract (English)
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.
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