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## Getting started
Start by cloning the repo:
```bash
git clone --depth 1 git@github.com:YuliangXiu/ECON.git
cd ECON
```
## Environment
- Ubuntu 20 / 18, (Windows as well, see [issue#7](https://github.com/YuliangXiu/ECON/issues/7))
- **CUDA=11.4, 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 (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
# 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}
}
```
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