From Pixels to Newtons: Predicting In Vivo Joint Contact Forces from Monocular Video

2026-06-08 · Source: cs.CV updates on arXiv.org · Field: Health & Wellbeing — Health & Medical Research, Medical Devices & Health Technology, Clinical Care & Medical Practice · Depth: Expert, extended

Summary

A novel physics-free pipeline accurately predicts instantaneous 3D hip and knee contact forces directly from uncalibrated monocular video, eliminating the need for markers, force plates, or musculoskeletal models. This system recovers parametric body meshes per frame, encoding them as kinematic features for a transformer model. The transformer's pose stream is adaptively modulated by body shape, joint, side, activity text, and V-JEPA 2 self-supervised video tokens, unifying hip and knee prediction. Validated via leave-one-subject-out cross-validation across 26 patients and 25 activity categories from the in vivo OrthoLoad database, the pipeline achieves an accuracy matching subject-specific musculoskeletal simulations (0.32± 0.08 BW RMSE for hip; 0.23± 0.03 BW for knee). It also resolves peak force changes relevant to gait retraining and osteoarthritis progression. Applied zero-shot, it rivals or outperforms prior methods, demonstrating transferability. Furthermore, self-supervised video features alone maintain accuracy, removing a manual labeling bottleneck. The pipeline also drives a generative motion prior to identify load-reducing movement strategies.

Key takeaway

For clinical biomechanists and rehabilitation specialists seeking scalable, non-invasive joint loading assessment, this video-based pipeline provides laboratory-grade accuracy without complex instrumentation. You can utilize uncalibrated monocular video to predict hip and knee contact forces, enabling retrospective analysis of archived recordings or real-time tracking during rehabilitation. Consider integrating its generative inverse design capabilities to identify patient-specific, load-reducing motion strategies, streamlining intervention planning.

Key insights

A physics-free pipeline accurately predicts in vivo hip and knee contact forces from monocular video, matching traditional simulation.

Principles

• End-to-end learning from in vivo data can match complex biomechanical simulation accuracy.
• Self-supervised video features effectively substitute for curated activity labels.
• Differentiable force predictors enable gradient-based inverse design for motion optimization.

Method

Parametric body meshes are recovered from video, encoded as kinematic features, and fed to a transformer modulated by body shape, joint, side, and V-JEPA 2 video tokens, outputting 3D forces and uncertainty.

In practice

• Estimate joint contact forces from standard video for clinical screening or rehabilitation.
• Integrate V-JEPA 2 features to automate activity context extraction.
• Apply gradient-guided generation to identify load-reducing movement strategies.

Topics

• Joint Contact Forces
• Monocular Video Analysis
• Biomechanics
• Transformer Models
• V-JEPA 2
• Motion Optimization
• Rehabilitation

Code references

• jeylau/jcf
• isarandi/nlf-pipeline

Best for: Computer Vision Engineer, Research Scientist, Machine Learning Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.