This repository contains the official implementation of the following paper:
**BeautyREC:Robust, Efficient, and Component-Specific Makeup Transfer. ** CVPRW 2023
Paper Link: paper
The pre-trained model is avaiable at "./checkpoints/BeautyREC.pt"
Put the VGG weights in "./network/REC/"
Data :
Beautyface parsing(vis): https://drive.google.com/file/d/1sRE-VvC63Cyn_VNUOKYO4WY762_qEjX2/view?usp=sharing
Beautyface parsing maps: https://drive.google.com/file/d/1WgadvcV1pUtEMCYxjwWBledEQfDbadn7/view?usp=sharing
Environments:
python >= 3.6
torch >= 1.0
tensorboardX >= 1.6
utils-misc >= 0.0.5
mscv >= 0.0.3
Put the train-list of makeup images in "./mtdataset/makeup.txt" and the train-list of non-makeup images in "./mtdataset/nomakeup.txt" ( you can randomly split the images.)
train the mt dataset using "./makeuploader/dataset.py"
train the wild dataset using "./makeuploader/wilddataset.py"
You can init the dataset in "./makeuploader/dataloaders.py"
CUDA_VISIBLE_DEVICES=0 python train.py --tag mt --model REC Put the test-list of makeup images in "./mtdataset/makeup_test.txt" and the test-list of non-makeup images in "./mtdataset/nomakeup_test.txt"
CUDA_VISIBLE_DEVICES=0 python test.py --model REC --load checkpoints/BeautyREC.pt