MuyBridge：基于单目视频的移动端人体质心估计，通过稀疏融合实现

中文摘要 AI 辅助

三维质心（CoM）是运动、康复及临床运动生物力学分析中的核心参数，但现有三维姿态跟踪、网格恢复及多视图三角测量方法要么仅优化三维关键点精度而缺乏解剖学约束，要么计算量与采集基础设施过重，难以在CoM跟踪最具应用价值的场景部署。因此，教练与运动分析师仍难以从运动员训练和比赛时使用的单台相机中测量出可量化的CoM。本研究提出MuyBridge，一种可在设备上运行的系统，能从单台手机相机的视频流中估计运动员的分段质心轨迹。MuyBridge将紧凑的二维姿态网络与蒸馏后的单步单目深度网络，通过结合解剖学与物理先验的解析度量融合方式耦合，以此锚定度量CoM，无需三维或任务特定的监督。在AthletePose3D数据集的运动动作（跑步、田径及花样滑冰）上评估，经一次校准后，MuyBridge的垂直CoM误差为33-41毫米，绝对相对范围误差（AbsRel）为2.3-6.6%；在iPhone 15设备上，采用异步2.86Hz的深度更新，可实现63 FPS的姿态估计速率生成CoM估计结果。代码可从指定网址获取。

英文摘要

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

发表机构

• ETH Zurich（苏黎世联邦理工学院）
• University of Graz（格拉茨大学）
• University of Bologna（博洛尼亚大学）

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