| ## Getting started | |
| Start by cloning the repo: | |
| ```bash | |
| git clone https://github.com/YadiraF/PIXIE | |
| cd PIXIE | |
| ``` | |
| #### Requirements | |
| * Python 3.7 (numpy, skimage, scipy, opencv, kornia) | |
| * PyTorch >= 1.6 | |
| You can run | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| Or create a separate virtual environment by running: | |
| ```bash | |
| bash install_conda.sh | |
| ``` | |
| or | |
| ```bash | |
| bash install_pip.sh | |
| ``` | |
| For visualization, we use our [rasterizer](https://github.com/YadiraF/PIXIE/tree/master/pixielib/utils/rasterizer) that uses pytorch JIT Compiling Extensions. | |
| If there occurs a compiling error, you can install [pytorch3d](https://github.com/facebookresearch/pytorch3d/blob/master/INSTALL.md) instead and set --rasterizer_type=pytorch3d when running the demos. | |
| #### Pre-trained model and data | |
| * Register [SMPL-X Model](http://smpl-x.is.tue.mpg.de/) | |
| * Register [PIXIE data](http://pixie.is.tue.mpg.de/) | |
| ```bash | |
| bash fetch_model.sh # username & password are required | |
| ``` | |
| * (Optional) Follow the instructions for the [Albedo model](https://github.com/TimoBolkart/BFM_to_FLAME) to get 'FLAME_albedo_from_BFM.npz'. Put it into `./data` | |
| * (Optional) Clone and prepare [DECA](https://github.com/YadiraF/DECA) | |