mlsanityData to 13 Oct 2026

OpenCap Monocular: 3D Human Kinematics and Musculoskeletal Dynamics from a Single Smartphone Video

arXiv 2603.24733 ↗First posted 25 Mar 2026cs.CV eess.IV q-bio.QMcited by 8

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Abstract (arXiv)

Quantifying human movement (kinematics) and musculoskeletal forces (kinetics) at scale, such as estimating quadriceps force during a sit-to-stand movement, could transform prediction, treatment, and monitoring of mobility-related conditions. However, quantifying kinematics and kinetics traditionally requires costly, time-intensive analysis in specialized laboratories, limiting clinical translation. Scalable, accurate tools for biomechanical assessment are needed. We introduce OpenCap Monocular, an algorithm that estimates 3D skeletal kinematics and kinetics from a single smartphone video. The method refines 3D human pose estimates from a monocular pose estimation model (WHAM) via optimization, computes kinematics of a biomechanically constrained skeletal model, and estimates kinetics via physics-based simulation and machine learning. We validated OpenCap Monocular against marker-based motion capture and force plate data for walking, squatting, and sit-to-stand tasks. OpenCap Monocular achieved low kinematic error (4.8deg mean absolute error for rotational degrees of freedom; 3.4 cm for pelvis translations), outperforming a regression-only computer vision baseline by 48% in rotational accuracy (p = 0.036) and 69% in translational accuracy (p < 0.001). OpenCap Monocular also estimated ground reaction forces during walking with accuracy comparable to, or better than, our prior two-camera OpenCap system. We demonstrate that the algorithm estimates important kinetic outcomes with clinically meaningful accuracy in applications related to frailty and knee osteoarthritis, including estimating knee extension moment during sit-to-stand transitions and knee adduction moment during walking. OpenCap Monocular is deployed via a smartphone app, web app, and secure cloud computing (https://opencap.ai), enabling free, accessible single-smartphone biomechanical assessments.

Key facts. Cited by 8 papers in the map (data through 13 Oct 2026). Builds on 7 of its 61 references that are in the map. First posted on arXiv 25 Mar 2026.

Builds on

7 of 61 references.

References · 7

  1. 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,418
  2. 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,161
  3. 2025VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understandingcited by 510
  4. 2022ViTPose: Simple Vision Transformer Baselines for Human Pose Estimationcited by 452
  5. 2021HuMoR: 3D Human Motion Model for Robust Pose Estimationcited by 321
  6. 2020A review of 3D human pose estimation algorithms for markerless motion capturecited by 24
  7. 2024AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scalecited by 11

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2026: 8.

8 papers.

  1. 2026Biomechanical 3D Body: Self-Supervised Distillation of Biomechanical Pose from a 3D Body Foundation Modelcited by 1
  2. 2026BioHuman: Learning Biomechanical Human Representations from Videocited by 0
  3. 2026AIGaitor: Privacy-preserving and cloud-free motion analysis for everyone, using edge computingcited by 0
  4. 2026From Pixels to Newtons: Predicting In Vivo Joint Contact Forces from Monocular Videocited by 0
  5. 2026Direct Clinical Joint Angle Extraction from Parametric Body Model Rotation Matricescited by 0
  6. 2026SynthGait-19K: A Physically Grounded Synthetic Video Dataset for Gait Parameter Estimationcited by 0
  7. 2026PACT: End-to-End Learning of Human Pose, Contacts, and Forces from Videocited by 0
  8. 2026Monocular markerless biomechanics for clinically interpretable gait assessment in spinal cord injurycited by 0