用闪烁LED实现毫秒级人体动作捕捉，突破传统方法成本与精度瓶颈。
FlashCap: Millisecond-Accurate Human Motion Capture via Flashing LEDs and Event-Based Vision
- 基于闪烁LED与事件视觉，实现毫秒级动作时间同步。
- 构建多模态数据集FlashMotion，定位误差降低约40%。
- 适合运动分析、人机交互等对时间精度要求高的场景。
精确动作时间（PMT）对快速动作分析至关重要，毫秒级差异可能决定体育赛事胜负。尽管人体姿态估计（HPE）进展显著，但高时间分辨率标注数据集稀缺，导致PMT长期被忽视。当前依赖高速RGB相机实现的PMT仅限于奥运会等特殊场景，受限于高成本、光敏感性、带宽和计算复杂度，难以日常使用。本文提出首个基于闪烁LED的动捕系统FlashCap，成功采集毫秒级分辨率的人体动作数据集FlashMotion，包含事件、RGB、LiDAR和IMU多模态数据，并通过严格验证其高质量。为评估数据价值，开展两项任务：精确动作时间与高时间分辨率HPE。为此提出ResPose基线模型，基于事件与RGB学习残差姿态。实验表明，ResPose将姿态估计误差降低约40%，达到毫秒级时间精度，开启新研究可能。数据与代码将公开共享。
原文摘要 · Abstract (English)
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.
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