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Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation. CVPR 2019
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README.md

Art2Real

This repository contains the reference code for the paper Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation (CVPR 2019).

Please cite with the following BibTeX:

@inproceedings{tomei2019art2real,
  title={{Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation}},
  author={Tomei, Matteo and Cornia, Marcella and Baraldi, Lorenzo and Cucchiara, Rita},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2019}
}

Art2Real

Requirements

This code is built on top of the Cycle-GAN source code.

The required Python packages are:

  • torch>=0.4.1
  • torchvision>=0.2.1
  • dominate>=2.3.1
  • visdom>=0.1.8.3

Pre-trained Models

Download pre-trained models and place them under the checkpoint folder. For example, when downloading the monet2photo checkpoints, place them under the folder ./checkpoints/monet2photo/.

Test

Run python test.py using the following arguments:

Argument Possible values
--dataroot Dataset root folder containing the testA directory
--name monet2photo, landscape2photo, portrait2photo
--num_test Number of test samples

For example, to reproduce the results of our model for the first 100 test samples of the landscape2photo setting, use:

python test.py --dataroot ./datasets/landscape2photo --name landscape2photo --num_test 100

Training

The training code will be available soon.

Art2Real Art2Real

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