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Matcaffe implementation of our CVPR17paper on face completion.

• We use thecaffe-for-cudnn-v2.5.48. Please referCaffe for more installation details.
• Basically, you need to first modify theMATLAB_DIR in Makefile.config and then run the following commands for a successful compilation:

make all -j4make matcaffe

• Follow theDCGAN to prepare the data (CelebA). The only differece is that the face we cropped is of size 128x128. Please modify Line 10 in theircrop_celebA.lua file. We use the standard train&test split of theCelebA dataset.

• Modify the training data path in ./matlab/FaceCompletion_training/GFC_caffeinit.m file.

• Download our face parsing modelModel_parsing and put it under ./matlab/FaceCompletion_training/model/ folder.

• We provide an initialmodel that is only trained with the reconstruction loss, as a good start point for the subsequent GAN training. Please download it and put it under ./matlab/FaceCompletion_training/model/ folder.

• Run ./matlab/FaceCompletion_training/demo_GFC_training.m for training.

• Download our face completion modelModel_G and put it under ./matlab/FaceCompletion_testing/model/ folder.
• Run ./matlab/FaceCompletion_testing/demo_face128.m for completion. TestImages are from the CelebA test dataset.

@inproceedings{GFC-CVPR-2017,    author = {Li, Yijun and Liu, Sifei and Yang, Jimei and Yang, Ming-Hsuan},    title = {Generative Face Completion},    booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},    year = {2017}}

• Gratitude goes toSifei Liu for the great help on code.
• Theupsample layer (unpooling according to the pooling mask) is borrowed from theSegNet.