A implementation of Disentangled Makeup Transfer with Generative Adversarial Network.

1. Download MT (Makeup Transfer) dataset from here.

2. Put MT (Makeup Transfer) dataset to .\data\RawData. Your data path will like this:

.\data\RawData\images\makeup\*.png
.\data\RawData\images\non-makeup\*.png

.\data\RawData\segs\makeup\*.png
.\data\RawData\segs\non-makeup\*.png

3. run python convert.py

4. Modify train.py and start training.
   python train.py

5. run python export.py and you will get h5 model in .\Export.

1. make sure you have run python export.py to get h5 model.

2. Modify demo.py and run python demo.py, you will find the transfer result in .\Transfer.

1. The test environment is

   • Python 3.7
   • tensorflow-gpu 2.0.0
   • tensorflow-addons 0.7.1
   • imgaug 0.4.0

2. This is still not a completed implementation, but almost 95% is the same as paper described.