├── assets ├── intro.png └── intro_new.png ├── LICENSE └── README.md /assets/intro.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Inowlzy/RadarLLM/HEAD/assets/intro.png -------------------------------------------------------------------------------- /assets/intro_new.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Inowlzy/RadarLLM/HEAD/assets/intro_new.png -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2025 Zengyuan Lai 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |

RadarLLM: Empowering Large Language Models to Understand Human Motion from Millimeter-Wave Point Cloud Sequence

2 | 3 |

15 |

16 | ¹Shanghai Jiao Tong University 17 | ²Bytedacne Research 18 |
19 | ^*Equal contribution 20 |
21 | ^†Corresponding author 22 |
23 |

24 |

🤩 Accepted to AAAI 2026 🤩

25 |

26 | 27 | 28 |

29 |

30 |

31 |

32 |

33 | This is the PyTorch implementation of our paper RadarLLM at AAAI 2026. 34 |

35 | 36 | ## TODO 37 | - [ ] Release code and pretrained model. (We are making every effort to accelerate the preparation and approval of relevant materials.) 38 | 39 | ## 🚀 Getting Started 40 | 41 | ### 1. Environment Setup 42 | 43 | We tested our environment on `Ubuntu 20.04 LTS` with `CUDA 11.4`. 44 | 45 | ```bash 46 | coming soon! 47 | ``` 48 | 49 | ### 2. Prepare Body Model and Weights 50 | 51 | Download [SMPL-X](https://smpl-x.is.tue.mpg.de/) and put the models under `body_model/` folder. The structure of `body_model/` folder should be: 52 | 53 | ``` 54 | coming soon! 55 | ``` 56 | 57 | 58 | 59 | 60 | ## 📖 Citation 61 | If you find our code or paper helps, please consider citing: 62 | ```bibtex 63 | Please waiting! 64 | ``` 65 | 66 | ## Acknowledgments 67 | 68 | Thanks to the following work that we refer to and benefit from: 69 | - [MotionGPT](https://github.com/OpenMotionLab/MotionGPT): the overall framework; 70 | - [T5](https://github.com/google-research/text-to-text-transfer-transformer): the language model; 71 | - [RF-Genesis](https://github.com/Asixa/RF-Genesis): the virtual generation framework; 72 | - [MaST](https://github.com/JohnsonSign/MaST-Pre): the point cloud feature extractor framework; 73 | - [mmMesh](https://github.com/HavocFiXer/mmMesh): point cloud grouping inspiration. 74 | 75 | 76 | ## Licenses 77 |

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 78 | --------------------------------------------------------------------------------