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Lipstick ain't enough: Beyond Color-Matching for In-the-Wild Makeup Transfer (CVPR 2021)

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Table of Content
  1. Introduction
  2. Datasets
  3. Getting Started
  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!

teaser.png
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.

Open In Colab - arXiv - project page


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-Real.png
CPM-Synt-1 5555 synthesis - makeup images with pattern segmentation mask ./imgs/CPM-Synt-1.png
CPM-Synt-2 1625 synthesis - triplets: makeup, non-makeup, ground-truth ./imgs/CPM-Synt-2.png
Stickers 577 high-quality images with alpha channel Stickers.png

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
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
mkdir checkpoints
cd checkpoints
wget https://public.vinai.io/CPM_checkpoints/color.pth
wget https://public.vinai.io/CPM_checkpoints/pattern.pth
  • Download [PRNet pre-trained model] from Drive. Put it in PRNet/net-data
Usage

➡️ You can now try it in Google Colab Open in 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

result
From left to right: Style, Input & Output

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
    

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