Training V2

Data preparation

Download the IJB-C dataset (used in the paper) or alternatively any other source of high resolution videos will be fine as well.

The videos should be placed in a flat directory structure.

Run the following command to preprocess all the videos:

cd fsgan/preprocess
python preprocess_video.py <path/to/videos/directory>

This process is slow and can take several days. To speed up the preprocessing it is best to run on multiple GPUs. For example, preprocessing 1,000 videos on 2 GPUs:

# GPU 0
export CUDA_VISIBLE_DEVICES=0
python preprocess_video.py -i ":500" <path/to/videos/directory>

# GPU 1
export CUDA_VISIBLE_DEVICES=1
python preprocess_video.py -i "500:" <path/to/videos/directory>

Finally, provide a list of video files for the training and validation sets or generate them automatically using the following script:

python produce_train_val.py <path/to/videos/directory>

Training face reenactment

Edit the following variables in fsgan/experiments/reenactment/ijbc_msrunet_reenactment_attr_no_seg.py configuration file:

• root - path to the videos dataset
• train_dataset and val_dataset - adjust the paths to the split files relative to root.
• batch_size - should be as large as possiple limited to the available GPUs memory.
• workers - should be the number of available CPU cores.

To start the training run:

cd fsgan/experiments/reenactment
python ijbc_msrunet_reenactment_attr_no_seg.py

Training face inpainting

After obtaining a face reenactment model, it can be used in the training of the face completion model.

Edit the fsgan/experiments/swapping/ijbc_msrunet_inpainting.py configuration files. In addition to the variables mentioned for the face reenactment training, make sure reenactment_model is set to the path of trained face reenactment model.

To start the training run:

cd fsgan/experiments/swapping
python ijbc_msrunet_inpainting.py

Training face blending

Like the face inpaintining training, this step should follow the face reenactment training.

Edit the fsgan/experiments/swapping/ijbc_msrunet_blending.py configuration files. In addition to the variables mentioned for the face reenactment training, make sure reenactment_model is set to the path of trained face reenactment model.

To start the training run:

cd fsgan/experiments/swapping
python ijbc_msrunet_blending.py