Please download the Dataset and copy to the source folder.

1. Requirements
PyTorch >= 0.4.1 (PyTorch v1.1.0 is tested successfully on macOS and Linux.)
Python >= 3.6 (Numpy, Scipy, Matplotlib)
Dlib (Dlib is optionally for face and landmarks detection. There is no need to use Dlib if you can provide face bouding bbox and landmarks. Besides, you can try the two-step inference strategy without initialized landmarks.)
OpenCV (Python version, for image IO operations.)
Cython (For accelerating depth and PNCC render.) 


pip install torch torchvision # for cpu version. more option to see https://pytorch.org
pip install numpy scipy matplotlib
pip install dlib==19.5.0 # 19.15+ version may cause conflict with pytorch in Linux, this may take several minutes. If 19.5 version raises errors, you may try 19.15+ version.
pip install opencv-python
pip install cython

2. Build cython module (just one line for building)
cd MM3D/cython
python3 setup.py build_ext -i

3. Run the main.py with arbitrary image as input
python main.py -f examples/images/test.jpg

4. To run the train.py: python train.py