This code has only been tested on Ubuntu.
Clone this repository:
git clone https://github.com/MarilynKeller/OSSO
cd OSSO
Create a virtual environment and activate it:
python3.8 -m venv osso_venv
source osso_venv/bin/activate
Install the required packages:
pip install --upgrade pip
pip install -r requirements.txt
From OSSO folder, execute:
git clone https://github.com/silviazuffi/gloss_skeleton.git
You should have the following folder structure:
OSSO/
├── data/
├── figures/
├── gloss_skeleton/
│ ├── gloss/
│ └── models/
└── osso/
├── star_model/
└── utils/
With the virtual environment sourced, run:
git clone https://github.com/MPI-IS/mesh.git
cd /path/to/mesh
make all
The compilation takes some minutes.
Download STAR from the website https://star.is.tue.mpg.de/downloads. You will need to create an account.
Place the extracted files in the data
folder.
cd path/to/OSSO/data
unzip star_1_1.zip
Likewise, download the Skeleton Inference Model (first link) from https://osso.is.tue.mpg.de/download.php, and place it in the data
folder.
unzip skeleton.zip
Your OSSO/data
folder should look like this:
data/
├── demo/
├── loss/
├── skeleton/
│ ├── betas_regressor_female.pkl
│ ├── betas_regressor_male.pkl
│ ├── ldm_indices.pkl
│ ├── ldm_regressor_female.pkl
│ ├── ldm_regressor_male.pkl
│ ├── lying_pose_female.pkl
│ ├── lying_pose_male.pkl
│ ├── skeleton_model.pkl
│ ├── skeleton_pca_female.pkl
│ └── skeleton_pca_male.pkl
└── star_1_1/
├── female/
│ └── model.npz
├── LICENSE.txt
├── male/
│ └── model.npz
└── neutral/
└── model.npz
cd path/to/OSSO
pip install .