Makeup Transformation Network Results

Input Images
Input NonMakeup 1 Input NonMakeup 2 Input NonMakeup 3 Input Makeup 1 Input Makeup 2 Input Makeup 2

Results:

Applied Makeup 1 Applied Makeup 2 Applied Makeup 3 Removed Makeup 1 Removed Makeup 2 Removed Makeup 3
Implementation of a basic CycleGAN network trained on existing makeup datasets, however, the majority of images in these datasets are of the full-frontal face. Considered the baseline for the comparison.
Training Details
Model CycleGAN
Epochs 200
Image Dimension 256 x 256px
Generator Architecture 9 Block Resnet
Discriminator Architecture PatchGan
# Makeup Images 504
# Nonmakeup Images 283
View Training Progress Images
Applied Makeup 1 Applied Makeup 2 Applied Makeup 3 Removed Makeup 1 Removed Makeup 2 Removed Makeup 3
Implementation of a basic CycleGAN network trained with existing frontal-face makeup datasets and the new collected dataset including faces with a range of pose.
Training Details
Model CycleGAN
Epochs 200
Image Dimension 256 x 256px
Generator Architecture 9 Block Resnet
Discriminator Architecture PatchGan
# Makeup Images (Frontal) 504
# Nonmakeup Images(Frontal) 283
# Makeup Images (Pose) 252
# Nonmakeup Images (Pose) 237
View Training Progress Images
Applied Makeup 1 Applied Makeup 2 Applied Makeup 3 Removed Makeup 1 Removed Makeup 2 Removed Makeup 3
Implementation of a basic CycleGAN network trained with only the new collected dataset which includes faces with and without makeup in a range of pose.
Training Details
Model CycleGAN
Epochs 200
Image Dimension 256 x 256px
Generator Architecture 9 Block Resnet
Discriminator Architecture PatchGan
# Makeup Images (Pose) 504
# Nonmakeup Images (Pose) 237
View Training Progress Images
Applied Makeup 1 Applied Makeup 2 Applied Makeup 3 Removed Makeup 1 Removed Makeup 2 Removed Makeup 3
Implementation of a basic CycleGAN network trained with only the new collected dataset which includes faces with and without makeup in a range of pose. A 6-block ResNet architecture is used for the generator.
A ResNet architecture has the following features:
  • Skip Connections
Training Details
Model CycleGAN
Epochs 200
Image Dimension 256 x 256px
Generator Architecture 6 Block Resnet
Discriminator Architecture PatchGan
# Makeup Images (Pose) 252
# Nonmakeup Images (Pose) 237
View Training Progress Images
Applied Makeup 1 Applied Makeup 2 Applied Makeup 3 Removed Makeup 1 Removed Makeup 2 Removed Makeup 3
Implementation of a basic CycleGAN network trained with only the new collected dataset which includes faces with and without makeup in a range of pose. The Unet-256 architecture is used.
A ResNet architecture has the following features:
  • Skip Connections
Training Details
Model CycleGAN
Epochs 200
Image Dimension 256 x 256px
Generator Architecture Unet-256
Discriminator Architecture PatchGan
# Makeup Images (Pose) 252
# Nonmakeup Images (Pose) 237
View Training Progress Images
Applied Makeup 1 Applied Makeup 2 Applied Makeup 3 Removed Makeup 1 Removed Makeup 2 Removed Makeup 3
A network trained with the InstaGAN model. The model is built on top of CycleGAN but includes the use of segmentation masks to mark relevant instances to transform. An additional loss function encourages to transfigure only the instances. Due to this, it might be helpful to highlight relevant facial features to assist the network in identifying the facial structure and pose.
Training Details
Model InstaGAN
Epochs 200
Image Dimension 200 x 200px
Generator Architecture 9 Block Resnet
Discriminator Architecture PatchGan
# Makeup Images (Frontal) 504
# Nonmakeup Images (Frontal) 283
# Makeup Images (Pose) 252
# Nonmakeup Images (Pose) 237