---
license: cc-by-nc-4.0
language:
- en
pretty_name: ForceBody
size_categories:
- 10K<n<100K
task_categories:
- other
tags:
- 3d-human-body-models
- biomechanics
- ground-reaction-force
- joint-torque
- skel
- human-motion
configs:
- config_name: default
  data_files:
  - split: train
    path: manifest_train.csv
  - split: test
    path: manifest_test.csv
---

<div align="center">

<h1>ForceBody: Force-Paired Parametric Body Motion with Torque Uncertainty</h1>

**NeurIPS 2026, Evaluations & Datasets Track**

Joonwoo Kwon<sup>1</sup>, Yufei Zhang<sup>2</sup>, Xiaoming Liu<sup>3</sup>, Zijun Cui<sup>1</sup>

<sup>1</sup>Michigan State University, <sup>2</sup>Independent Researcher, <sup>3</sup>University of North Carolina at Chapel Hill

<img src="assets/divider.png" width="100%" height="4">

### 💻 Code and benchmark on GitHub: [github.com/DominoAI-Lab/ForceBody-NeurIPS-ED-2026](https://github.com/DominoAI-Lab/ForceBody-NeurIPS-ED-2026)

</div>

<p align="center"><img src="assets/teaser.png" width="100%" alt="Fig. 1. SKEL body model, per-joint torque and torque uncertainty, and force and uncertainty annotations over a walking sequence."></p>

<sub><b>Fig. 1.</b> (a) SKEL, a biomechanical skeleton inside the SMPL surface. (b) One frame with bones colored by joint torque |τ| (left) and by its uncertainty σ<sub>τ</sub> (right). (c) Torque over a walking sequence. (d) Uncertainty over the same sequence. Green arrows are measured GRF.</sub>

<p align="center"><img src="assets/fig2_dataset.gif" width="100%" alt="Fig. 2. Dataset statistics and fourteen subjects at true metric scale with SKEL body, skeleton and GRF."></p>

<sub><b>Fig. 2.</b> (a) BMI, body mass and height by sex across 140 subjects, and activity composition of 10,386 trials. (b) Fourteen subjects in one scene at true metric scale, each with their own SKEL body shape, skeleton and measured GRF (green). Labels give sex, height and body mass.</sub>

ForceBody pairs SKEL body motion with measured ground reaction forces (GRF), inverse-dynamics joint torques, and a per-frame, per-joint torque uncertainty σ<sub>τ</sub>. This page holds the data. Code, loader and benchmark are on GitHub. The source data come from [AddBiomechanics](https://addbiomechanics.org/). The Croissant metadata, with Responsible AI fields, is in [croissant.json](https://huggingface.co/datasets/kwonjoon/ForceBody/resolve/main/croissant.json).

| Trials | Subjects | Hours | Frames | Sources | Release |
|:---:|:---:|:---:|:---:|:---:|:---:|
| 10,386 | 140 | 26.9 | 9.7M | 10 studies (AddBiomechanics) | 1.3.0 (2026-10-29) |

<img src="assets/divider.png" width="100%" height="4">

## Demos

| Running | Stair ascent |
|:---:|:---:|
| <img src="assets/running_hamner2013.gif" width="100%"> | <img src="assets/stair_ascent_falisse2017.gif" width="100%"> |
| **Sit-to-stand** | **Drop jump** |
| <img src="assets/sit_to_stand_uhlrich2023.gif" width="100%"> | <img src="assets/drop_jump.gif" width="100%"> |

**Torque |τ| (front skeleton) and uncertainty σ<sub>τ</sub> (back skeleton) across six activities (paper Fig. 6)**

<p align="center"><img src="assets/fig6_activities.gif" width="100%"></p>

**Fit quality at the acceleration level, AddBiomechanics (black) vs. SKEL fit (orange) (paper Fig. 5)**

<p align="center"><img src="assets/fig5_dof_acceleration.gif" width="100%"></p>

**Torque τ, SKEL-recomputed torque τ<sub>SKEL</sub> and the band τ ± σ<sub>τ</sub> (paper Fig. 7)**

<p align="center"><img src="assets/fig7_running_sts.gif" width="100%"></p>

Animated versions of paper Figs. 2(b), 5, 6 and 7 are in [`assets/paper_figures/`](https://huggingface.co/datasets/kwonjoon/ForceBody/tree/main/assets/paper_figures).

<img src="assets/divider.png" width="100%" height="4">

## Quick start

### 0. Download the SKEL model (required)

The trial files store SKEL parameters (`poses`, `betas`, `trans`), not meshes. Register and download the SKEL model from the [SKEL website](https://skel.is.tue.mpg.de/) under its non-commercial license. ForceBody does not redistribute it. You need it only to build meshes. The joint positions (`joints`), force, torque and uncertainty arrays can be read without it.

### 1. Download the data

```bash
pip install -U huggingface_hub
hf download kwonjoon/ForceBody --repo-type dataset --local-dir ForceBody
```

The full dataset is about 46 GB (43 GiB). Keep about 50 GB of disk free. You do not need a Hugging Face login, and an `HF_TOKEN` only raises the rate limit.

To try one trial first (9 MB), run this command instead of the `hf download` line above. Steps 2 and 3 then run unchanged.

```bash
hf download kwonjoon/ForceBody trials/Moore2015/S10/Moore2015_S10_walk_014.npz --repo-type dataset --local-dir ForceBody
```

### 2. Install the loader

Run these commands in the folder where you ran step 1, so that `ForceBody/` and `ForceBody-NeurIPS-ED-2026/` are side by side.

```bash
git clone https://github.com/DominoAI-Lab/ForceBody-NeurIPS-ED-2026.git
cd ForceBody-NeurIPS-ED-2026/code
pip install numpy
```

### 3. Load a trial

Run Python in `ForceBody-NeurIPS-ED-2026/code`. The data folder of step 1 is then `../../ForceBody`.

```python
import forcebody_io as fio

d = fio.load_trial("../../ForceBody/trials/Moore2015/S10/Moore2015_S10_walk_014.npz", pose_source="pass0")
m = float(d["mass_kg_dynamics"])
ok = d["mc_frame_ok"] & bool(d["torque_label_usable"])   # frame mask and trial flag
tau   = d["tau_selfconsistent"][ok, 6:] / m   # joint torques (N m / kg), columns 0-5 are root residuals
sigma = d["sigma_tau"][ok, 6:] / m            # torque uncertainty (N m / kg)
print(tau.shape, sigma.shape)
```

For force or torque prediction, feed the model `pose_source="pass0"`. Combine every frame mask with its trial flag.

### 4. Train and evaluate

See the GitHub repository (`code/baselines/`) for cache building, training, uncertainty-weighted finetuning, inference and scoring.

<img src="assets/divider.png" width="100%" height="4">

## What is in a trial

T = frames. C = contact channels: 2 (right foot, left foot), or 3 in Falisse2017, where channel 2 is the pelvis on an instrumented chair.

| Field | Shape | Content |
|---|---|---|
| `poses`, `trans`, `betas` | `[T,46]`, `[T,3]`, `[10]` | SKEL pose, root translation, body shape |
| `grf_aligned`, `cop_aligned_foot` | `[T,C,3]` | Measured GRF (N) and centre of pressure (m) per contact channel |
| `tau_selfconsistent` | `[T,37]` | Inverse dynamics in AddBiomechanics DOF order (`dof_names`). Columns 0-5 are pelvis root residuals (0-2 moments in N m, 3-5 forces in N). Columns 6-36 are joint torques (N m) |
| `sigma_tau`, `mu_tau` | `[T,37]` | Monte Carlo torque uncertainty and mean, same columns |
| `grf_frame_ok`, `tau_frame_ok`, `mc_frame_ok` | `[T]` | Frame masks for GRF, torque and σ<sub>τ</sub> |
| `cop_frame_ok`, `grf_foot_ok` | `[T,2]` | Per-foot frame masks (right, left) |
| `force_label_usable`, `torque_label_usable` | scalar | Trial flags |

Three σ<sub>τ</sub> counts appear in the release. The trial files carry σ<sub>τ</sub> for 10,382 of the 10,386 trials (`has_uncertainty`). The other 4 trials are shorter than one period of the 6 Hz noise band. The σ<sub>τ</sub> statistics of the paper (Sec. 3.2, Table 2, Appendix D) use the 8,732 trials with `has_uncertainty` and GRF valid on at least 50% of frames (`grf_valid_frac` >= 0.5). The paper calls them the trials with released σ<sub>τ</sub>. The `subjects.csv` column `n_with_uncertainty` holds the v1.1 count (8,652 trials in total). Use `has_uncertainty` in `manifest.csv` instead.

<img src="assets/divider.png" width="100%" height="4">

## Splits

The splits are at subject level (`split_addb`). The Hugging Face split `train` (`manifest_train.csv`) holds 121 subjects and 9,472 trials. It includes the 12 validation subjects (1,098 trials, `split_addb_val == "val"`) that the paper uses for checkpoint selection. The split `test` (`manifest_test.csv`) holds 19 subjects and 914 trials. Select checkpoints on the validation subjects only. DETAILS.md ("Splits") gives the split rules and the text-to-motion split `split_t2m`.

<img src="assets/divider.png" width="100%" height="4">

## Files

| Path | Content |
|---|---|
| `trials/<study>/S<NN>/<trial_uid>.npz` | One file per trial |
| `models/` | AddBiomechanics OpenSim models (`.osim.gz`) per source b3d file, processing passes 0 and 2 (the torques use the pass-2 model) |
| `manifest.csv` (+ `_train`, `_test`) | One row per trial: metadata, split, quality flags |
| `persons.csv` | One row per subject: SKEL shape (`beta_0` to `beta_9`), sex, height, mass, body measurements, splits |
| `subjects.csv` | One row per subject: age, health status, footwear, mass sources, trial counts, activities (`n_with_uncertainty` is the v1.1 count, see DETAILS.md) |
| `SCHEMA_v1.3.csv` | Dtype, shape, unit, frame and description of all 547 trial fields and of every `manifest.csv` and `persons.csv` column |
| `DETAILS.md` | Full documentation (masks, conventions, known issues) |
| `CHANGELOG.md`, `MD5SUMS.txt` | Version history, md5 checksums |
| [`croissant.json`](https://huggingface.co/datasets/kwonjoon/ForceBody/resolve/main/croissant.json) | Croissant metadata with Responsible AI fields |
| `LICENSE.md` | License and attribution terms |
| Other files | Gait events, key maps and marker map (DETAILS.md, "Layout") |

<img src="assets/divider.png" width="100%" height="4">

## License

Data under **CC BY-NC 4.0** (non-commercial). SKEL-derived fields also follow the [SKEL license](https://skel.is.tue.mpg.de/). Code on GitHub under MIT. Cite [AddBiomechanics](https://addbiomechanics.org/) and the source studies you use. DETAILS.md ("How to cite") gives the BibTeX entries and the source-study references. LICENSE.md gives the full license and attribution terms.

<img src="assets/divider.png" width="100%" height="4">

## Issues and contact

Please report errors and questions on the GitHub issues page [github.com/DominoAI-Lab/ForceBody-NeurIPS-ED-2026/issues](https://github.com/DominoAI-Lab/ForceBody-NeurIPS-ED-2026/issues). CHANGELOG.md records every correction.

<img src="assets/divider.png" width="100%" height="4">

## Citation

```bibtex
@inproceedings{kwon2026forcebody,
  title     = {ForceBody: Force-Paired Parametric Body Motion with Torque Uncertainty},
  author    = {Kwon, Joonwoo and Zhang, Yufei and Liu, Xiaoming and Cui, Zijun},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
  year      = {2026}
}
```