FlashCap：基于闪烁 LED 与事件视觉的毫秒级精确人体运动捕捉
FlashCap: Millisecond-Accurate Human Motion Capture via Flashing LEDs and Event-Based Vision
用闪烁 LED 作为事件相机的高频时间戳信号源，配合 RGB 学习残差姿态，为高时序分辨率人体姿态估计提供了一套低成本、可复用的数据采集与建模范式。
原文依据 we propose ResPose, a simple yet effective baseline that learns residual poses based on events and RGBs
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论精确运动计时（PMT）对快速运动分析至关重要。一毫秒的差异可能决定体育比赛的胜负。尽管人体姿态估计（HPE）已取得显著进展，但由于高时间分辨率标注数据集有限，PMT 在很大程度上仍被 HPE 社区忽视。目前，PMT 依赖高速 RGB 相机在奥运会等特定场景中实现；然而，其高成本、光敏性、带宽和计算复杂度限制了其在日常使用中的可行性。我们开发了 FlashCap，首个基于闪烁 LED 的 PMT 运动捕捉系统。借助 FlashCap，我们采集了毫秒级分辨率的人体运动数据集 FlashMotion，包含事件、RGB、LiDAR 和 IMU 四种模态，并通过严格验证证明了其高质量。为评估 FlashMotion 的价值，我们开展了两项任务：精确运动计时和高时间分辨率 HPE。针对这两项任务，我们提出了 ResPose，一个简单而有效的基线，基于事件和 RGB 学习残差姿态。实验结果表明，ResPose 将姿态估计误差降低 40%，并达到毫秒级计时精度，从而开辟了新的研究机会。数据集和代码将向社区共享。
英文原文摘要 1299 字符
Precise motion timing (PMT) is crucial for swift motion analysis. A millisecond difference may determine victory or defeat in sports competitions. Despite substantial progress in human pose estimation (HPE), PMT remains largely overlooked by the HPE community due to the limited availability of high-temporal-resolution labeled datasets. Today, PMT is achieved using high-speed RGB cameras in specialized scenarios such as the Olympic Games; however, their high costs, light sensitivity, bandwidth, and computational complexity limit their feasibility for daily use. We developed FlashCap, the first flashing LED-based MoCap system for PMT. With FlashCap, we collect a millisecond-resolution human motion dataset, FlashMotion, comprising the event, RGB, LiDAR, and IMU modalities, and demonstrate its high quality through rigorous validation. To evaluate the merits of FlashMotion, we perform two tasks: precise motion timing and high-temporal-resolution HPE. For these tasks, we propose ResPose, a simple yet effective baseline that learns residual poses based on events and RGBs. Experimental results show that ResPose reduces pose estimation errors by 40% and achieves millisecond-level timing accuracy, enabling new research opportunities. The dataset and code will be shared with the community.