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# RF Genesis ### [Project Page](https://rfgen.xingyuchen.me/) | [Paper](https://xingyuchen.me/files/Xingyu.Chen_SenSys23_RFGen.pdf) The offical implementation of [ *RF Genesis: Zero-Shot Generalization of mmWave Sensing through Simulation-Based Data Synthesis and Generative Diffusion Models*](https://rfgen.xingyuchen.me/). [Xingyu Chen](https://xingyuchen.me/), [Xinyu Zhang](http://xyzhang.ucsd.edu/index.html), UC San Diego. In SenSys 2023  ## News 📢 **22/Jan/24** - Initial Release of RF Genesis! 📢 **29/March/24** - Added the code for point-cloud processing and visualization. ## To-Do List - [ ] Release the RFLoRA pretrained model. - [ ] Release the RFLoRA training dataset and procedures. - [ ] More documentations. ## Quick Start This code was tested on `Ubuntu 20.04.5 LTS` and requires: * Python 3.10 * conda3 or miniconda3 * CUDA capable GPU (one is enough) Clone the repository ``` git clone https://github.com/Asixa/RF-Genesis.git cd RF-Genesis ``` Create a conda environment. ``` conda create -n rfgen python=3.10 -y conda activate rfgen ``` Install python packages ``` pip install -r requirements.txt sh setup.sh ``` Run a simple example. ``` python run.py -o "a person walking back and forth" -e "a living room" -n "hello_rfgen" ``` ## Visualization  Rendered SMPL animation and radar point clouds. ## Radar Hardware The current simulation is based on the model of [**Texas Instruments AWR 1843**](https://www.ti.com/product/AWR1843#all) radar, with 3TX 4RX MIMO setup.  The radar configuration is shown in [TI1843.json](https://github.com/Asixa/RF-Genesis/blob/main/models/TI1843_config.json) and it can be freely adjusted. ## Citation ``` @inproceedings{chen2023rfgenesis, author = {Chen, Xingyu and Zhang, Xinyu}, title = {RF Genesis: Zero-Shot Generalization of mmWave Sensing through Simulation-Based Data Synthesis and Generative Diffusion Models}, booktitle = {ACM Conference on Embedded Networked Sensor Systems (SenSys ’23)}, year = {2023}, pages = {1-14}, address = {Istanbul, Turkiye}, publisher = {ACM, New York, NY, USA}, url = {https://doi.org/10.1145/3625687.3625798}, doi = {10.1145/3625687.3625798} } ``` ## License This code is distributed under an [MIT LICENSE](LICENSE). Note that our code depends on other libraries, including [CLIP](https://github.com/openai/CLIP), [SMPL](https://smpl.is.tue.mpg.de/), [MDM](https://guytevet.github.io/mdm-page/), [mmMesh](https://github.com/HavocFiXer/mmMesh) and uses datasets that each have their own respective licenses that must also be followed.