metadata
license: other
task_categories:
- robotics
tags:
- motion-retargeting
- musclemimic
- mujoco
- amass
pretty_name: MuscleMimic GMR-Fit Retargeted Motions
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This dataset is derived from AMASS and is provided only for permitted
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non-commercial education, or non-commercial artistic projects.
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MuscleMimic GMR Retargeted Motions
Pre-retargeted motion capture data for the MyoFullBody musculoskeletal model, generated using General Motion Retargeting (GMR).
Dataset Groups
| Group | Motions | Description |
|---|---|---|
| KIT_KINESIS_TRAINING_MOTIONS | 972 | KIT locomotion training set |
| KIT_KINESIS_TESTING_MOTIONS | 108 | KIT locomotion test set |
| AMASS_TRANSITION_MOTIONS | 110 | AMASS action transitions |
Usage
# Auto-download (integrated into training pipeline)
# Just set retargeting_method: gmr in your config - caches download automatically.
# Manual download
uv run musclemimic-download-gmr-caches --dataset-group KIT_KINESIS_TRAINING_MOTIONS
# Python API
from musclemimic.utils import download_gmr_dataset_group
download_gmr_dataset_group("KIT_KINESIS_TRAINING_MOTIONS")
File Format
Each .npz file contains retargeted motion data with arrays:
qpos, qvel, xpos, xquat, cvel, subtree_com, site_xpos, site_xmat,
plus metadata (joint_names, body_names, site_names, frequency, split_points).
Source
Derived from the AMASS dataset. Please comply with the AMASS license terms when using this data.
Paper: https://huggingface.co/papers/2603.25544
bibTeX
@article{li2026towards,
title={Towards Embodied AI with MuscleMimic: Unlocking full-body musculoskeletal motor learning at scale},
author={Li, Chengkun and Wang, Cheryl and Ziliotto, Bianca and Simos, Merkourios and Kovecses, Jozsef and Durandau, Guillaume and Mathis, Alexander},
journal={arXiv preprint arXiv:2603.25544},
year={2026}
}