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.

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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

<div style="align: left; text-align:center;"> <img src="./result.png" alt="result" width="250"/> <div class="caption">From left to right: Style, Input & Output</div> </div>

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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.

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🌿 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
   

<!-- **Lipstick ain't enough: Beyond Color Matching for In-the-Wild Makeup Transfer**. \ T. Nguyen, A. Tran, M. Hoai (2021) \ IEEE Conference on Computer Vision and Pattern Recognition (CVPR). -->

Docker file

docker build -t name .