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Table of Content

1. Introduction
2. Datasets
3. Getting Started
   • Requirements
   • Usage Example
4. Training & Evaluation

CPM: Color-Pattern Makeup Transfer

• CPM is a holistic makeup transfer framework that outperforms previous state-of-the-art models on both light and extreme makeup styles.
• CPM consists of an improved color transfer branch (based on BeautyGAN) and a novel pattern transfer branch.
• We also introduce 4 new datasets (both real and synthesis) to train and evaluate CPM.

📢 New: We provide ❝Qualitative Performane Comparisons❞ online! Check it out!

CPM can replicate both colors and patterns from a reference makeup style to another image.

Details of the dataset construction, model architecture, and experimental results can be found in our following paper:

@inproceedings{m_Nguyen-etal-CVPR21,
  author = {Thao Nguyen and Anh Tran and Minh Hoai},
  title = {Lipstick ain't enough: Beyond Color Matching for In-the-Wild Makeup Transfer},
  year = {2021},
  booktitle = {Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition (CVPR)}
}

Please CITE our paper whenever our datasets or model implementation is used to help produce published results or incorporated into other software.

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Datasets

We introduce ✨ 4 new datasets: CPM-Real, CPM-Synt-1, CPM-Synt-2, and Stickers datasets. Besides, we also use published LADN's Dataset & Makeup Transfer Dataset.

CPM-Real and Stickers are crawled from Google Image Search, while CPM-Synt-1 & 2 are built on Makeup Transfer and Stickers. (Click on dataset name to download)

Name	#imgs	Description	-
CPM-Real	3895	real - makeup styles
CPM-Synt-1	5555	synthesis - makeup images with pattern segmentation mask
CPM-Synt-2	1625	synthesis - triplets: makeup, non-makeup, ground-truth
Stickers	577	high-quality images with alpha channel

Dataset Folder Structure can be found here.

By downloading these datasets, USER agrees:

• to use these datasets for research or educational purposes only
• to not distribute the datasets or part of the datasets in any original or modified form.
• and to cite our paper whenever these datasets are employed to help produce published results.

Getting Started

Requirements

• python=3.7
• torch==1.6.0
• tensorflow-gpu==1.14
• segmentation_models_pytorch

Installation

# clone the repo
git clone https://github.com/VinAIResearch/CPM.git
cd CPM

# install dependencies
conda env create -f environment.yml

Download pre-trained models

• Download CPM’s pre-trained models: color.pth and pattern.pth. Put them in checkpoints folder.

• Download [PRNet pre-trained model] from Drive. Put it in PRNet/net-data

Usage

➡️ You can now try it in Google Colab

# Color+Pattern: 
CUDA_VISIBLE_DEVICES=0 python main.py --style ./imgs/style-1.png --input ./imgs/non-makeup.png

# Color Only: 
CUDA_VISIBLE_DEVICES=0 python main.py --style ./imgs/style-1.png --input ./imgs/non-makeup.png --color_only

# Pattern Only: 
CUDA_VISIBLE_DEVICES=0 python main.py --style ./imgs/style-1.png --input ./imgs/non-makeup.png --pattern_only

Result image will be saved in result.png

Training and Evaluation

As stated in the paper, the Color Branch and Pattern Branch are totally independent. Yet, they shared the same workflow:

1. Data preparation: Generating texture_map of faces.

2. Training

Please redirect to Color Branch or Pattern Branch for further details.

🌿 If you have trouble running the code, please read Trouble Shooting before creating an issue. Thank you 🌿

Trouble Shooting

1. [Solved] ImportError: libGL.so.1: cannot open shared object file: No such file or directory:

   sudo apt update
   sudo apt install libgl1-mesa-glx
   

2. [Solved] RuntimeError: Expected tensor for argument #1 'input' to have the same device as tensor for argument #2 'weight'; but device 1 does not equal 0 (while checking arguments for cudnn_convolution) Add CUDA VISIBLE DEVICES before .py. Ex:

   CUDA_VISIBLE_DEVICES=0 python main.py
   

3. [Solved] RuntimeError: cuda runtime error (999) : unknown error at /opt/conda/conda-bld/pytorch_1595629403081/work/aten/src/THC/THCGeneral.cpp:47

   sudo rmmod nvidia_uvm
   sudo modprobe nvidia_uvm
   

Docker file

docker build -t name .