Official implementation of Cross-Domain Face Synthesis using a Controllable GAN

This page contains end-to-end demo code that generates a set of synthetic face images under a specified pose from an unconstrained 2D face image based on the information obtained from target domain.

Prerequisite

Download the Basel Face Model** and move 01_MorphableModel.mat into the folder.

Instructions for Windows 10

Install:

install MeshLab 2016.12

add "C:\Program Files\VCG\MeshLab" to the environmental variable "path"

conda create -n CGAN python=3.6
conda activate CGAN

pip install dlib
pip install pyglet
pip install pywavefront
pip install opencv-python
pip install imutils
pip install  https://pypi.python.org/packages/da/06/bd3e241c4eb0a662914b3b4875fc52dd176a9db0d4a2c915ac2ad8800e9e/dlib-19.7.0-cp36-cp36m-win_amd64.whl#md5=b7330a5b2d46420343fbed5df69e6a3f

pip install matplotlib
pip install keras==2.2.5
pip install tensorflow-gpu==1.14

pip install git+https://www.github.com/keras-team/keras-contrib.git

• Generate 3D simulated images from still images:

Put the still images in "./face3d/input/", while each identity is in a seperate folder. Run:

cd face3d
pyhton face3d.py
python pre.py
cd ..

3D rendered results will be in:

"face3d/output"

• Use C-GAN to refine the 3D simulated images:

Put the 3D simulated data in:

./data/sim

Put the target data in:

data/chokepoint/target

Run:

python cgan.py

Results will be in:

"./output"

Data

• ChokePoint
• COX-S2V

Citation

If you find this work useful, please cite our paper with the following bibtex:

@InProceedings{Mokhayeri_2020_WACV, author = {Mokhayeri, Fania and Kamali, Kaveh and Granger, Eric}, title = {Cross-Domain Face Synthesis using a Controllable GAN}, booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)}, year = {2020} }