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ForceBody

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metadata 
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

ForceBody: Force-Paired Parametric Body Motion with Torque Uncertainty

NeurIPS 2026, Evaluations & Datasets Track

Joonwoo Kwon1, Yufei Zhang2, Xiaoming Liu3, Zijun Cui1

1Michigan State University, 2Independent Researcher, 3University of North Carolina at Chapel Hill

💻 Code and benchmark on GitHub: github.com/DominoAI-Lab/ForceBody-NeurIPS-ED-2026

ForceBody pairs SKEL body motion with measured ground reaction forces (GRF), inverse-dynamics joint torques, and a per-frame, per-joint torque uncertainty στ. This page holds the data. Code, loader and benchmark are on GitHub. The source data come from AddBiomechanics. The Croissant metadata, with Responsible AI fields, is in croissant.json.

Trials Subjects Hours Frames Sources Release
10,386 140 27 9.7M 10 studies (AddBiomechanics) 1.3.0 (2026-10-29)

Demos

Running Stair ascent
Sit-to-stand Drop jump

Torque |τ| (left body) and uncertainty στ (right body)

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 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 

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.

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.

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.

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.

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 στ
cop_frame_ok, grf_foot_ok [T,2] Per-foot frame masks (right, left)
force_label_usable, torque_label_usable scalar Trial flags

Three στ counts appear in the release. The trial files carry στ 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 στ 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 στ. The subjects.csv column n_with_uncertainty holds the v1.1 count (8,652 trials in total). Use has_uncertainty in manifest.csv instead.

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.

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 Croissant metadata with Responsible AI fields
LICENSE.md License and attribution terms
Other files Gait events, key maps and marker map (DETAILS.md, "Layout")

License

Data under CC BY-NC 4.0 (non-commercial). SKEL-derived fields also follow the SKEL license. Code on GitHub under MIT. Cite AddBiomechanics 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.

Issues and contact

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

Citation 

@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}
}