Rolandos Alexandros Potamias1 Jinglei Zhang2 Jiankang Deng1 Stefanos Zafeiriou1
1Imperial College London, UK
2Shanghai Jiao Tong University, China
This is the official implementation of WiLoR, an state-of-the-art hand localization and reconstruction model:
git clone --recursive https://github.com/rolpotamias/WiLoR.git
cd WiLoR
The code has been tested with PyTorch 2.0.0 and CUDA 11.7. It is suggested to use an anaconda encironment to install the the required dependencies:
conda create --name wilor python=3.10
conda activate wilor
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu117
# Install requirements
pip install -r requirements.txt
Download the pretrained models using:
wget https://huggingface.co/spaces/rolpotamias/WiLoR/resolve/main/pretrained_models/detector.pt -P ./pretrained_models/
wget https://huggingface.co/spaces/rolpotamias/WiLoR/resolve/main/pretrained_models/wilor_final.ckpt -P ./pretrained_models/
It is also required to download MANO model from MANO website.
Create an account by clicking Sign Up and download the models (mano_v*_*.zip). Unzip and place the right hand model MANO_RIGHT.pkl
under the mano_data/
folder.
Note that MANO model falls under the MANO license.
python demo.py --img_folder demo_img --out_folder demo_out --save_mesh
You can start a local demo for inference by running:
python gradio_demo.py
The dataset will be released soon.
Parts of the code are taken or adapted from the following repos:
WiLoR models fall under the CC-BY-NC--ND License. This repository depends also on Ultralytics library and MANO Model, which are fall under their own licenses. By using this repository, you must also comply with the terms of these external licenses.
If you find WiLoR useful for your research, please consider citing our paper:
@misc{potamias2024wilor,
title={WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild},
author={Rolandos Alexandros Potamias and Jinglei Zhang and Jiankang Deng and Stefanos Zafeiriou},
year={2024},
eprint={2409.12259},
archivePrefix={arXiv},
primaryClass={cs.CV}
}