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README.md

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+---
+license: cc-by-nc-4.0
+tags:
+  - biomechanics
+  - human-pose
+  - joint-contact-force
+  - smpl
+  - video
+---
+
+# JCF — Joint Contact Forces from Monocular Video
+
+Predicts 3D hip and knee joint contact forces from a monocular video, via SMPL
+pose features (and optional frozen V-JEPA 2 video features). Accompanies
+*"From Pixels to Newtons: Predicting In Vivo Joint Contact Forces from Monocular Video"* ([arXiv](https://arxiv.org/abs/2606.06631)) ·
+code: [jeylau/jcf](https://github.com/jeylau/jcf).
+
+## Files
+- `model.pt` — force-predictor weights + architecture config
+- `feat_stats.npz` — feature normalisation statistics (required)
+- `text_vocab.json`, `text_embeddings.npy` — optional, for activity-label conditioning
+
+## Usage
+
+```bash
+pip install git+https://github.com/jeylau/jcf.git
+```
+
+```python
+from jcf import ForceModel
+
+model = ForceModel.from_pretrained("jeylau/jcf")
+result = model.predict("trial.npz", joint="knee", side="right")
+result.forces   # (T, 3) in bodyweight (BW) units;  also .time, .sigma
+```
+
+`trial.npz` is a preprocessed SMPL sequence; see the repo for the video → features pipeline.
+
+## Intended use & limitations
+Research use only. Trained on the OrthoLoad
+instrumented-implant cohort (26 patients, 25 activities) and evaluated zero-shot
+on an independent cohort (the Grand Challenge knee load competition data) where
+it matches or outperforms prior published methods. Accuracy
+outside these tested conditions (other activities or populations)
+is not guaranteed.
+
+## Citation
+```bibtex
+@article{lauer2026pixels,
+  title   = {From Pixels to Newtons: Predicting In Vivo Joint Contact Forces from Monocular Video},
+  author  = {Jessy Lauer},
+  journal = {arXiv preprint arXiv:2606.06631},
+  year    = {2026}
+}
+```