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SuperSVG: Superpixel-based Scalable Vector Graphics Synthesis

Paper | Suppl

Teng Hu, Ran Yi, Baihong Qian, Jiangning Zhang, Paul L. Rosin, and Yu-Kun Lai

Prepare

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 ..

Training Step

(0) Prepare

Data prepare: Download the ImageNet dataset.

(1) Train the Coarse-stage Model

Put the downloaded Imagenet or any dataset you want into $path_to_the_dataset. Then, you can train the coarse-stage model by running:

python3 main_coarse.py --data_path=$path_to_the_dataset

After training, the checkpoints and logs are saved in the directory output_coarse.

(2) Train the Refinement-stage Model

Coming soon

Citation

If you find this code helpful for your research, please cite:

@inproceedings{hu2024supersvg,
      title={SuperSVG: Superpixel-based Scalable Vector Graphics Synthesis}, 
      author={Teng Hu and Ran Yi and Baihong Qian and Jiangning Zhang and Paul L. Rosin and Yu-Kun Lai},
      booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
      year={2024}
}