| ## Getting started | |
| Start by cloning the repo: | |
| ```bash | |
| git clone --depth 1 [email protected]:YuliangXiu/ECON.git | |
| cd ECON | |
| ``` | |
| ## Environment | |
| - Ubuntu 20 / 18, (Windows as well, see [issue#7](https://github.com/YuliangXiu/ECON/issues/7)) | |
| - **CUDA=11.6, GPU Memory > 12GB** | |
| - Python = 3.8 | |
| - PyTorch >= 1.13.0 (official [Get Started](https://pytorch.org/get-started/locally/)) | |
| - Cupy >= 11.3.0 (offcial [Installation](https://docs.cupy.dev/en/stable/install.html#installing-cupy-from-pypi)) | |
| - PyTorch3D = 0.7.1 (official [INSTALL.md](https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md), recommend [install-from-local-clone](https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md#2-install-from-a-local-clone)) | |
| ```bash | |
| sudo apt-get install libeigen3-dev ffmpeg | |
| # install required packages | |
| cd ECON | |
| conda env create -f environment.yaml | |
| conda activate econ | |
| pip install -r requirements.txt | |
| # the installation(incl. compilation) of PyTorch3D will take ~20min | |
| pip install git+https://github.com/facebookresearch/[email protected] | |
| # install libmesh & libvoxelize | |
| cd lib/common/libmesh | |
| python setup.py build_ext --inplace | |
| cd ../libvoxelize | |
| python setup.py build_ext --inplace | |
| ``` | |
| ## Register at [ICON's website](https://icon.is.tue.mpg.de/) | |
|  | |
| Required: | |
| - [SMPL](http://smpl.is.tue.mpg.de/): SMPL Model (Male, Female) | |
| - [SMPL-X](http://smpl-x.is.tue.mpg.de/): SMPL-X Model, used for training | |
| - [SMPLIFY](http://smplify.is.tue.mpg.de/): SMPL Model (Neutral) | |
| - [PIXIE](https://icon.is.tue.mpg.de/user.php): PIXIE SMPL-X estimator | |
| :warning: Click **Register now** on all dependencies, then you can download them all with **ONE** account. | |
| ## Downloading required models and extra data | |
| ```bash | |
| cd ECON | |
| bash fetch_data.sh # requires username and password | |
| ``` | |
| ## Citation | |
| :+1: Please consider citing these awesome HPS approaches: PyMAF-X, PIXIE | |
| ``` | |
| @article{pymafx2022, | |
| title={PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images}, | |
| author={Zhang, Hongwen and Tian, Yating and Zhang, Yuxiang and Li, Mengcheng and An, Liang and Sun, Zhenan and Liu, Yebin}, | |
| journal={arXiv preprint arXiv:2207.06400}, | |
| year={2022} | |
| } | |
| @inproceedings{PIXIE:2021, | |
| title={Collaborative Regression of Expressive Bodies using Moderation}, | |
| author={Yao Feng and Vasileios Choutas and Timo Bolkart and Dimitrios Tzionas and Michael J. Black}, | |
| booktitle={International Conference on 3D Vision (3DV)}, | |
| year={2021} | |
| } | |
| ``` | |