FlashCap: Millisecond-Accurate Human Motion Capture via Flashing LEDs and Event-Based Vision

TL;DR

FlashCap introduces a novel LED-flashing motion capture system that achieves millisecond-accurate human motion timing and high-temporal-resolution pose estimation, filling a critical gap in human pose analysis.

Contribution

The paper presents FlashCap, the first flashing LED-based motion capture system, along with a high-resolution dataset and a baseline method for precise timing and pose estimation.

Findings

ResPose reduces pose estimation errors by ~40%.

Achieves millisecond-level motion timing accuracy.

Enables new research in high-temporal-resolution human pose analysis.

Abstract

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.