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## 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)
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