LIVE- Towards Layer-wise Image Vectorization (CVPR 2022 Oral)

Xu Ma, Yuqian Zhou, Xingqian Xu, Bin Sun, Valerii Filev, Nikita Orlov, Yun Fu, Humphrey Shi

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Primary contact: Xu Ma

Installation

We suggest users to use the conda for creating new python environment.

Requirement: 5.0<GCC<6.0; nvcc >10.0.

git clone https://github.com/Picsart-AI-Research/LIVE-Layerwise-Image-Vectorization.git
cd LIVE-Layerwise-Image-Vectorization
conda create -n live python=3.7
conda activate live
conda install -y pytorch torchvision -c pytorch
conda install -y numpy scikit-image
conda install -y -c anaconda cmake
conda install -y -c conda-forge ffmpeg
pip install svgwrite svgpathtools cssutils numba torch-tools scikit-fmm easydict visdom
pip install opencv-python==4.5.4.60  # please install this version to avoid segmentation fault.

cd DiffVG
git submodule update --init --recursive
python setup.py install
cd ..

Run Experiments

conda activate live
cd LIVE
# Please modify the paramters accordingly.
python main.py --config <config.yaml> --experiment <experiment-setting> --signature <given-folder-name> --target <input-image> --log_dir <log-dir>
# Here is an simple example:
python main.py --config config/base.yaml --experiment experiment_5x1 --signature smile --target figures/smile.png --log_dir log/

Reference

@inproceedings{xu2022live,
    title={Towards Layer-wise Image Vectorization},
    author={Ma, Xu and Zhou, Yuqian and Xu, Xingqian and Sun, Bin and Filev, Valerii and  Orlov, Nikita and Fu, Yun and Shi, Humphrey},
    booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
    year={2022}
}

Acknowledgement

Our implementation is mainly based on the diffvg codebase. We gratefully thank the authors for their wonderful works.

LICENSE

LIVE is under the Apache-2.0 license. Please contact the authors for commercial use.