README.md

metadata

pretty_name: MotionMillion
size_categories:
  - n<1T
task_categories:
  - other
language:
  - en
tags:
  - Large Human Motion
  - Humanoid
  - Humanoid Locomotion
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  ### MotionMillion COMMUNITY LICENSE AGREEMENT

  MotionMillion Release Date: July 30, 2025 All the data and code within this
  repo are under [CC BY-NC-SA
  4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
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🔑 Key Features

• Over 2000 hours of high-quality human motion captured from web-scale human video data, covering:
  • Martial Arts (23.7%)
  • Fitness (26.4%)
  • Performance (17.5%)
  • Dance (14.9%)
  • Non-Human (2.9%)
  • Sports (2.4%)
• Over 20 detailed annotations per motion, including:
  • Age
  • Body Characteristics
  • Movement Styles
  • Emotions
  • Environments

Table of Contents

• Key Features
• Get Started
  • Download the Dataset
  • Dataset Structure
• License and Citation

👨‍🏫 Get Started

Download the Dataset

To download the full dataset, use the following code. If you encounter any issues, refer to the official Hugging Face documentation.

# Ensure git-lfs is installed (https://git-lfs.com)
git lfs install
# When prompted for a password, use an access token with write permissions.
# Generate one in your settings: https://huggingface.co/settings/tokens
git clone https://huggingface.co/datasets/InternRobotics/MotionMillion
# To clone without large files (only their pointers)
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/InternRobotics/MotionMillion

If you only need to download a specific dataset (e.g., MotionGV/folder1.tar.gz), use the following code:

# Ensure git-lfs is installed (https://git-lfs.com)
git lfs install
# Initialize an empty Git repository
git init MotionMillion
cd MotionMillion
# Set the remote repository
git remote add origin https://huggingface.co/datasets/InternRobotics/MotionMillion
# Enable sparse-checkout
git sparse-checkout init
# Specify the target folders and files
git sparse-checkout set MotionGV/folder1.tar.gz
# Pull the data
git pull origin main

Dataset Processing

Folder Hierarchy

MotionMillion
|-- motion_272rpr
|   |-- Mirror_MotionGV
|   |   |-- folder0.tar.gz
|   |   |-- folder1.tar.gz
|   |   |-- folder2.tar.gz
|   |   |-- folder3.tar.gz
|   |   |-- folder4.tar.gz
|   |   |-- folder5.tar.gz
|   |   |-- folder6.tar.gz
|   |   |-- folder7.tar.gz
|   |   |-- folder8.tar.gz
|   |   `-- folder9.tar.gz
|   |-- Mirror_MotionLLAMA
|   |   |-- finedance.tar.gz
|   |   |-- fit3d.tar.gz
|   |   |-- hi4d.tar.gz
|   |   |-- humansc3d.tar.gz
|   |   |-- interhuman.tar.gz
|   |   |-- interx.tar.gz
|   |   `-- trumans.tar.gz
|   |-- Mirror_MotionUnion
|   |   |-- 100STYLE_smpl.tar.gz
|   |   |-- CombatMotion_seperate.tar.gz
|   |   |-- EgoBody.tar.gz
|   |   |-- animation.tar.gz
|   |   |-- fitness.tar.gz
|   |   |-- game_motion.tar.gz
|   |   |-- haa500.tar.gz
|   |   |-- humman.tar.gz
|   |   |-- idea400.tar.gz
|   |   |-- kungfu.tar.gz
|   |   |-- music.tar.gz
|   |   `-- perform.tar.gz
|   |-- MotionGV
|   |   |-- folder0.tar.gz
|   |   |-- folder1.tar.gz
|   |   |-- folder2.tar.gz
|   |   |-- folder3.tar.gz
|   |   |-- folder4.tar.gz
|   |   |-- folder5.tar.gz
|   |   |-- folder6.tar.gz
|   |   |-- folder7.tar.gz
|   |   |-- folder8.tar.gz
|   |   `-- folder9.tar.gz
|   |-- MotionLLAMA
|   |   |-- finedance.tar.gz
|   |   |-- fit3d.tar.gz
|   |   |-- hi4d.tar.gz
|   |   |-- humansc3d.tar.gz
|   |   |-- interhuman.tar.gz
|   |   |-- interx.tar.gz
|   |   `-- trumans.tar.gz
|   |-- MotionUnion
|   |   |-- 100STYLE_smpl.tar.gz
|   |   |-- CombatMotion_seperate.tar.gz
|   |   |-- EgoBody.tar.gz
|   |   |-- animation.tar.gz
|   |   |-- fitness.tar.gz
|   |   |-- game_motion.tar.gz
|   |   |-- haa500.tar.gz
|   |   |-- humman.tar.gz
|   |   |-- idea400.tar.gz
|   |   |-- kungfu.tar.gz
|   |   |-- music.tar.gz
|   |   `-- perform.tar.gz
|-- mean_std
│   |-- Mean.npy
│   `-- Std.npy
|-- texts.tar.gz
|-- splits.tar.gz

Due to data licensing restrictions, we only provide parts of the processed motion data with 272-representation. Among these, MotionGV contains motions captured by our motion capture algorithm; the remaining data is merged from other datasets.

Due to copyright constraints, BABEL, AIST and HumanML3D cannot be released directly. We will provide detailed data processing workflows.

Data Processing Steps

1. For all tar.gz files, use tar -xzvf x.tar.gz to extract them.
2. For HumanML3D, please refer to data_process/HumanML3D.
3. For BABEL, please refer to data_process/BABEL.
4. For AIST, please refer to data_process/AIST.

Processed Data Hierarchy

MotionMillion
|-- motion_272rpr
|   |-- BABEL
|   |-- Mirror_BABEL
|   |-- Mirror_MotionGV
|   |   |-- folder0
|   |   |-- folder1
|   |   |-- folder2
|   |   |-- folder3
|   |   |-- folder4
|   |   |-- folder5
|   |   |-- folder6
|   |   |-- folder7
|   |   |-- folder8
|   |   `-- folder9
|   |-- Mirror_MotionLLAMA
|   |   |-- aist
|   |   |-- finedance
|   |   |-- fit3d
|   |   |-- hi4d
|   |   |-- humansc3d
|   |   |-- interhuman
|   |   |-- interx
|   |   `-- trumans
|   |-- Mirror_MotionUnion
|   |   |-- 100STYLE_smpl
|   |   |-- CombatMotion_seperate
|   |   |-- EgoBody
|   |   |-- animation
|   |   |-- fitness
|   |   |-- game_motion
|   |   |-- haa500
|   |   |-- humanml
|   |   |-- humman
|   |   |-- idea400
|   |   |-- kungfu
|   |   |-- music
|   |   `-- perform
|   |-- Mirror_PhantomDanceDatav1.1
|   |-- MotionGV
|   |   |-- folder0
|   |   |-- folder1
|   |   |-- folder2
|   |   |-- folder3
|   |   |-- folder4
|   |   |-- folder5
|   |   |-- folder6
|   |   |-- folder7
|   |   |-- folder8
|   |   `-- folder9
|   |-- MotionLLAMA
|   |   |-- aist
|   |   |-- finedance
|   |   |-- fit3d
|   |   |-- hi4d
|   |   |-- humansc3d
|   |   |-- interhuman
|   |   |-- interx
|   |   `-- trumans
|   |-- MotionUnion
|   |   |-- 100STYLE_smpl
|   |   |-- CombatMotion_seperate
|   |   |-- EgoBody
|   |   |-- animation
|   |   |-- fitness
|   |   |-- game_motion
|   |   |-- haa500
|   |   |-- humanml
|   |   |-- humman
|   |   |-- idea400
|   |   |-- kungfu
|   |   |-- music
|   |   `-- perform
|   `-- PhantomDanceDatav1.1
|-- texts
|   |-- Mirror_MotionGV
|   |-- Mirror_MotionLLAMA
|   |-- Mirror_MotionUnion
|   |-- MotionGV
|   |-- MotionLLAMA
|   `-- MotionUnion
|-- mean_std
│   |-- Mean.npy
│   `-- Std.npy
|-- split
|   `-- version1
|       |-- t2m_60_300
|       |   |-- all.txt
|       |   |-- test.txt
|       |   |-- train.txt
|       |   `-- val.txt
|       `-- tokenizer_96
|           |-- test.txt
|           |-- train.txt
|           `-- val.txt

License and Citation

All the data and code within this repo are under CC BY-NC-SA 4.0. Please consider citing our project if it helps your research.

@article{fan2025go,
  title={Go to Zero: Towards Zero-shot Motion Generation with Million-scale Data},
  author={Fan, Ke and Lu, Shunlin and Dai, Minyue and Yu, Runyi and Xiao, Lixing and Dou, Zhiyang and Dong, Junting and Ma, Lizhuang and Wang, Jingbo},
  journal={arXiv preprint arXiv:2507.07095},
  year={2025}
}

In addition, please cite the following literature:

@article{xiao2025motionstreamer,
  title={MotionStreamer: Streaming Motion Generation via Diffusion-based Autoregressive Model in Causal Latent Space},
  author={Xiao, Lixing and Lu, Shunlin and Pi, Huaijin and Fan, Ke and Pan, Liang and Zhou, Yueer and Feng, Ziyong and Zhou, Xiaowei and Peng, Sida and Wang, Jingbo},
  journal={arXiv preprint arXiv:2503.15451},
  year={2025}
}

@inproceedings{amass,
  title={AMASS: Archive of motion capture as surface shapes},
  author={Mahmood, Naureen and Ghorbani, Nima and Troje, Nikolaus F and Pons-Moll, Gerard and Black, Michael J},
  booktitle={ICCV},
  pages={5442--5451},
  year={2019}
}

@InProceedings{Guo_2022_CVPR,
    author    = {Guo, Chuan and Zou, Shihao and Zuo, Xinxin and Wang, Sen and Ji, Wei and Li, Xingyu and Cheng, Li},
    title     = {Generating Diverse and Natural 3D Human Motions From Text},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {5152-5161}
}

@inproceedings{babel,
  title={BABEL: Bodies, action and behavior with english labels},
  author={Punnakkal, Abhinanda R and Chandrasekaran, Arjun and Athanasiou, Nikos and Quiros-Ramirez, Alejandra and Black, Michael J},
  booktitle={CVPR},
  pages={722--731},
  year={2021}
}

@inproceedings{flag3d,
  title={Flag3d: A 3d fitness activity dataset with language instruction},
  author={Tang, Yansong and Liu, Jinpeng and Liu, Aoyang and Yang, Bin and Dai, Wenxun and Rao, Yongming and Lu, Jiwen and Zhou, Jie and Li, Xiu},
  booktitle={CVPR},
  pages={22106--22117},
  year={2023}
}

@inproceedings{li2023finedance,
  title={FineDance: A Fine-grained Choreography Dataset for 3D Full Body Dance Generation},
  author={Li, Ronghui and Zhao, Junfan and Zhang, Yachao and Su, Mingyang and Ren, Zeping and Zhang, Han and Tang, Yansong and Li, Xiu},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={10234--10243},
  year={2023}
}

@article{motionx,
  title={Motion-x: A large-scale 3d expressive whole-body human motion dataset},
  author={Lin, Jing and Zeng, Ailing and Lu, Shunlin and Cai, Yuanhao and Zhang, Ruimao and Wang, Haoqian and Zhang, Lei},
  journal={NeurIPS},
  year={2024}
}

@article{liang2024intergen,
  title={Intergen: Diffusion-based multi-human motion generation under complex interactions},
  author={Liang, Han and Zhang, Wenqian and Li, Wenxuan and Yu, Jingyi and Xu, Lan},
  journal={International Journal of Computer Vision},
  volume={132},
  number={9},
  pages={3463--3483},
  year={2024},
  publisher={Springer}
}

@inproceedings{interx,
  title={Inter-x: Towards versatile human-human interaction analysis},
  author={Xu, Liang and Lv, Xintao and Yan, Yichao and Jin, Xin and Wu, Shuwen and Xu, Congsheng and Liu, Yifan and Zhou, Yizhou and Rao, Fengyun and Sheng, Xingdong and others},
  booktitle={CVPR},
  pages={22260--22271},
  year={2024}
}

@inproceedings{aist-dance-db,
    author = {Shuhei Tsuchida and Satoru Fukayama and Masahiro Hamasaki and Masataka Goto}, 
    title = {AIST Dance Video Database: Multi-genre, Multi-dancer, and Multi-camera Database for Dance Information Processing}, 
    booktitle = {Proceedings of the 20th International Society for Music Information Retrieval Conference, {ISMIR} 2019},
    address = {Delft, Netherlands}, 
    year = 2019, 
    month = nov}

@inproceedings{jiang2024scaling,
  title={Scaling up dynamic human-scene interaction modeling},
  author={Jiang, Nan and Zhang, Zhiyuan and Li, Hongjie and Ma, Xiaoxuan and Wang, Zan and Chen, Yixin and Liu, Tengyu and Zhu, Yixin and Huang, Siyuan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={1737--1747},
  year={2024}
}

@inproceedings{yin2023hi4d,
  title={Hi4d: 4d instance segmentation of close human interaction},
  author={Yin, Yifei and Guo, Chen and Kaufmann, Manuel and Zarate, Juan Jose and Song, Jie and Hilliges, Otmar},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={17016--17027},
  year={2023}
}

@inproceedings{fieraru2021learning,
  title={Learning complex 3D human self-contact},
  author={Fieraru, Mihai and Zanfir, Mihai and Oneata, Elisabeta and Popa, Alin-Ionut and Olaru, Vlad and Sminchisescu, Cristian},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={2},
  pages={1343--1351},
  year={2021}
}

@inproceedings{danceformer,
  title={Danceformer: Music conditioned 3d dance generation with parametric motion transformer},
  author={Li, Buyu and Zhao, Yongchi and Zhelun, Shi and Sheng, Lu},
  booktitle={AAAI},
  pages={1272--1279},
  year={2022}
}

Special Notes

• We would like to express our gratitude to the authors of FineDance for granting permission to directly open-source the preprocessed motion data. It is important to note that when generating the 272-dimensional motion representation, we utilized the SMPL-X data provided in MotionLLAMA with all beta values set to 0, which may differ from the original FineDance data.
• If you intend to use the merged data (excluding MotionGV), please strictly adhere to their respective licenses.