Problem
Research questions and friction points this paper is trying to address.
3D center of mass
biomechanical analysis
monocular video
single camera
athletic movements
MuyBridge: Mobile Human Center-of-Mass Estimation from Monocular Video via Sparse Fusion
🤖 AI Summary
本文提出MuyBridge系统，通过单手机摄像头视频流估计运动员质心轨迹，结合2D姿态网络和单步单目深度网络，解决传统方法计算量大、难以部署的问题。
📝 Abstract
The 3D center of mass (CoM) is a primary quantity in the biomechanical analysis of sport, rehabilitation, and clinical movement, yet existing 3D pose tracking, mesh recovery, and multi-view triangulation methods either optimize 3D keypoint accuracy without anatomical constraints or carry compute and capture infrastructure too heavy to deploy where CoM tracking is most useful. As a result, the metric CoM remains difficult for coaches and movement analysts to measure from a single camera where athletes train and compete. In this work, we introduce MuyBridge, an on-device system that estimates the athlete's segmental center of mass trajectory from a single phone camera video stream. MuyBridge couples a compact 2D pose network and a distilled single-step monocular depth network through an analytic metric fusion that uses anatomical and physical priors to anchor the metric CoM, requiring no 3D or task-specific supervision. Evaluated on the athletic movements of AthletePose3D (running, track and field, and figure skating), MuyBridge achieves 33-41 mm vertical CoM error and 2.3-6.6% absolute-relative range error (AbsRel) under a one-time calibration, and produces CoM estimates at the 63 FPS pose-estimation rate using asynchronous 2.86 Hz depth updates on iPhone 15. Code is available at: https://github.com/Abradshaw1/Muybridge
Innovation
Methods, ideas, or system contributions that make the work stand out.
monocular video
center of mass estimation
analytic metric fusion
on-device system
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