Notice: This codebase is the SMPL-X verison of the original MotionDiffuse work

For more information, please refer to our work FRoM-W1: Towards General Humanoid Whole-Body Control with Language Instructions.

We have devoted significant effort to training this version of the model very carefully. So if you find this version useful, please cite our work along with the original MotionDiffuse paper:

@article{DBLP:journals/corr/abs-2601-12799,
  author       = {Peng Li and
                  Zihan Zhuang and
                  Yangfan Gao and
                  Yi Dong and
                  Sixian Li and
                  Changhao Jiang and
                  Shihan Dou and
                  Zhiheng Xi and
                  Enyu Zhou and
                  Jixuan Huang and
                  Hui Li and
                  Jingjing Gong and
                  Xingjun Ma and
                  Tao Gui and
                  Zuxuan Wu and
                  Qi Zhang and
                  Xuanjing Huang and
                  Yu{-}Gang Jiang and
                  Xipeng Qiu},
  title        = {FRoM-W1: Towards General Humanoid Whole-Body Control with Language
                  Instructions},
  journal      = {CoRR},
  volume       = {abs/2601.12799},
  year         = {2026},
  url          = {https://doi.org/10.48550/arXiv.2601.12799},
  doi          = {10.48550/ARXIV.2601.12799},
  eprinttype   = {arXiv},
  eprint       = {2601.12799},
  timestamp    = {Tue, 24 Mar 2026 08:45:06 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2601-12799.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model

Mingyuan Zhang¹*  Zhongang Cai^1,2*  Liang Pan¹  Fangzhou Hong¹  Xinying Guo¹  Lei Yang²  Ziwei Liu¹⁺

¹S-Lab, Nanyang Technological University  ²SenseTime Research 

*equal contribution  ⁺corresponding author

play the guitar	walk sadly	walk happily	check time

This repository contains the official implementation of MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model.

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[Project Page] • [arXiv] • [Video] • [Colab Demo] • [Hugging Face Demo]

Updates

[10/2022] Add a 🤗Hugging Face Demo for text-driven motion generation!

[10/2022] Add a Colab Demo for text-driven motion generation!

[10/2022] Code release for text-driven motion generation!

[8/2022] Paper uploaded to arXiv.

Text-driven Motion Generation

You may refer to this file for detailed introduction.

Citation

If you find our work useful for your research, please consider citing the paper:

@article{zhang2022motiondiffuse,
  title={MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model},
  author={Zhang, Mingyuan and Cai, Zhongang and Pan, Liang and Hong, Fangzhou and Guo, Xinying and Yang, Lei and Liu, Ziwei},
  journal={arXiv preprint arXiv:2208.15001},
  year={2022}
}

Acknowledgements

This study is supported by NTU NAP, MOE AcRF Tier 2 (T2EP20221-0033), and under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s).