--- pretty_name: MotionMillion size_categories: - n<1T task_categories: - other language: - en tags: - Large Human Motion - Humanoid - Humanoid Locomotion extra_gated_prompt: >- ### 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/). extra_gated_fields: First Name: text Last Name: text Email: text Country: country Affiliation: text Phone: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other Research interest: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the InternData Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the InternData Privacy Policy. extra_gated_button_content: Submit --- # 🔑 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 Image Alt Text
# Table of Contents - [Key Features](#key-features-) - [Get Started](#get-started-) - [Download the Dataset](#download-the-dataset) - [Dataset Structure](#dataset-structure) - [License and Citation](#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. ```bash # 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: ```bash # 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](data_process/HumanML3D/README.md). 3. For BABEL, please refer to [data_process/BABEL](data_process/BABEL/README.md). 4. For AIST, please refer to [data_process/AIST](data_process/AIST/README.md). ### 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](https://creativecommons.org/licenses/by-nc-sa/4.0/). Please consider citing our project if it helps your research. ```BibTeX @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: ```BibTeX @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](https://github.com/li-ronghui/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](https://github.com/ZeyuLing/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.