用手机摄像头实时估算运动员重心轨迹，无需复杂设备
MuyBridge: Mobile Human Center-of-Mass Estimation from Monocular Video via Sparse Fusion
- 融合2D姿态与单步深度估计，利用解剖物理先验定位重心
- 垂直误差33-41毫米，相对范围误差2.3%-6.6%，单次校准即可
- 可在iPhone上实现63帧/秒姿态估计，适合教练和运动分析场景
三维重心（CoM）是运动、康复和临床动作分析中的核心指标。然而现有3D姿态追踪、网格恢复及多视角三角化方法要么仅优化关键点精度而缺乏解剖约束，要么依赖过重的计算与采集设备，难以在运动员训练和比赛场所部署。为此，本文提出MuyBridge，一个基于单部手机摄像头视频流的轻量级端侧系统，可估计运动员各肢体段重心轨迹。该系统通过解析式度量融合，将紧凑的2D姿态网络与蒸馏的单步单目深度网络结合，利用解剖与物理先验锚定度量重心，无需3D或任务特定监督。在AthletePose3D数据集（跑步、田径、花样滑冰）上的评估显示，经一次校准后，垂直方向重心误差为33-41毫米，绝对相对范围误差（AbsRel）为2.3%-6.6%；系统以63帧/秒的姿态估计速率运行，深度更新异步频率为2.86赫兹，适用于iPhone 15。代码已开源：https://github.com/Abradshaw1/Muybridge
原文摘要 · Abstract (English)
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
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