# Direct Clinical Joint Angle Extraction from Parametric Body Model Rotation Matrices

arXiv: https://arxiv.org/abs/2607.17639 · HTML: https://mlsanity.com/paper/2607.17639/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 39 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Builds on (most cited first, 19 of 19)

- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2011.14672/) (2020)
- [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020)
- [WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion](https://mlsanity.com/paper/2312.07531/) (2023)
- [SAM 3D Body: Robust Full-Body Human Mesh Recovery](https://mlsanity.com/paper/2602.15989/) (2026)
- [GENMO: A GENeralist Model for Human MOtion](https://mlsanity.com/paper/2505.01425/) (2025)
- [Kimodo: Scaling Controllable Human Motion Generation](https://mlsanity.com/paper/2603.15546/) (2026)
- [From Skin to Skeleton: Towards Biomechanically Accurate 3D Digital Humans](https://mlsanity.com/paper/2509.06607/) (2025)
- [Reconstructing Humans with a Biomechanically Accurate Skeleton](https://mlsanity.com/paper/2503.21751/) (2025)
- [BioPose: Biomechanically-accurate 3D Pose Estimation from Monocular Videos](https://mlsanity.com/paper/2501.07800/) (2025)
- [ATLAS: Decoupling Skeletal and Shape Parameters for Expressive Parametric Human Modeling](https://mlsanity.com/paper/2508.15767/) (2025)
- [Human Mesh Modeling for Anny Body](https://mlsanity.com/paper/2511.03589/) (2025)
- [Portable Biomechanics Laboratory: Clinically Accessible Movement Analysis from a Handheld Smartphone](https://mlsanity.com/paper/2507.08268/) (2025)
- [OpenCap Monocular: 3D Human Kinematics and Musculoskeletal Dynamics from a Single Smartphone Video](https://mlsanity.com/paper/2603.24733/) (2026)
- [SOMA: Unifying Parametric Human Body Models](https://mlsanity.com/paper/2603.16858/) (2026)
- [PoseBench: Benchmarking the Robustness of Pose Estimation Models under Corruptions](https://mlsanity.com/paper/2406.14367/) (2024)
- [Better Rigs, Not Bigger Networks: A Body Model Ablation for Gaussian Avatars](https://mlsanity.com/paper/2604.01447/) (2026)
- [Fall Risk and Gait Analysis in Community-Dwelling Older Adults using World-Spaced 3D Human Mesh Recovery](https://mlsanity.com/paper/2604.11961/) (2026)

## Abstract (arXiv)

Quantitative joint angles are rarely available in routine care because the tools are slow, costly, or confined to a laboratory. We show that clinical joint angles can be read directly from the per-segment rotation matrices a parametric body model already produces, with no inverse-kinematics or musculoskeletal-model fitting step. On the OpenCap LabValidation cohort, using the GEM-X body-model estimator on single-smartphone video, our pooled mean absolute error is 4.50 degrees over the fifteen joint angles that match the OpenCap Monocular reference set, the same accuracy range as OpenCap Monocular's 4.8 degrees on the same cohort and reference standard, from a much simpler pipeline. The step that connects a body model to clinical angles is a small calibration table rather than an optimisation, so the same procedure transfers unchanged to other body models: repeating it on SAM 3D Body, changing only the table, gives 4.66 degrees, statistically indistinguishable from GEM-X, and runs in real time from a live single-camera stream. The method needs no per-recording inputs beyond the video itself: no participant height, no camera-intrinsics database, no per-subject model scaling. This broadens where movement analysis is practical, from in-clinic and at-home recording to telerehabilitation and large-scale decentralised studies.