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AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks

Official Pytorch Implementation of 'AesPA-Net' (ICCV 2023)

(Note that this project is totally powerd by Yonsei University)

AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks

Paper[CVF] : Link

Paper[Arxiv] : Link

Abstract: To deliver the artistic expression of the target style, recent studies exploit the attention mechanism owing to its ability to map the local patches of the style image to the corresponding patches of the content image. However, because of the low semantic correspondence between arbitrary content and artworks, the attention module repeatedly abuses specific local patches from the style image, resulting in disharmonious and evident repetitive artifacts. To overcome this limitation and accomplish impeccable artistic style transfer, we focus on enhancing the attention mechanism and capturing the rhythm of patterns that organize the style. In this paper, we introduce a novel metric, namely pattern repeatability, that quantifies the repetition of patterns in the style image. Based on the pattern repeatability, we propose Aesthetic Pattern-Aware style transfer Networks (AesPA-Net) that discover the sweet spot of local and global style expressions. In addition, we propose a novel self-supervisory task to encourage the attention mechanism to learn precise and meaningful semantic correspondence. Lastly, we introduce the patch-wise style loss to transfer the elaborate rhythm of local patterns. Through qualitative and quantitative evaluations, we verify the reliability of the proposed pattern repeatability that aligns with human perception, and demonstrate the superiority of the proposed framework.

Prerequisites

Dependency

• Python==3.7
• CUDA==11.1
• Pytorch==1.7.1
• numpy==1.19.2
• Pillow==8.0.1
• imageio==2.9.0
• scipy==1.5.2

Usage

Set pretrained weights

• Pretrained models for encoder(VGG-19) can be found in the ./baseline_checkpoints
  • Pretrained VGG can be downloaded at vgg_normalised_conv5_1.t7

• Prepare pretrained models for AesPA-Net

  • Decoder can be downloaded at dec_model.pth
  • Transformer can be downloaded at transformer_model.pth

• Move these pretrained weights to each folders:

  • transformer.pth -> ./train_results/<comment>/log/transformer_model.pth
  • decoder.pth -> ./train_results/<comment>/log/dec_model.pth

Inference (Automatic)

bash scripts/test_styleaware_v2.sh

or

python main.py --type test --batch_size #batch_size --comment <comment> --content_dir <content_dir> --style_dir <style_dir> --num_workers #num_workers

Training

bash scripts/train_styleaware_v2.sh

Evaluation

Available soon

Citation

If you find this work useful for your research, please cite:

@InProceedings{Hong_2023_ICCV,
    author    = {Hong, Kibeom and Jeon, Seogkyu and Lee, Junsoo and Ahn, Namhyuk and Kim, Kunhee and Lee, Pilhyeon and Kim, Daesik and Uh, Youngjung and Byun, Hyeran},
    title     = {AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {22758-22767}
}

@article{Hong2023AesPANetAP,
  title={AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks},
  author={Kibeom Hong and Seogkyu Jeon and Junsoo Lee and Namhyuk Ahn and Kunhee Kim and Pilhyeon Lee and Daesik Kim and Youngjung Uh and Hyeran Byun},
  journal={ArXiv},
  year={2023},
  volume={abs/2307.09724},
  url={https://api.semanticscholar.org/CorpusID:259982728}
}

Contact

If you have any question or comment, please contact the first author of this paper - Kibeom Hong

kibeom9212@gmail.com