Paired-D GAN for Semantic Image Synthesis

This is a PyTorch implementation of the paper "Paired-D GAN for Semantic Image Synthesis", ACCV 2018.

Requirements

• PyTorch 0.4
• Torchvision
• Pillow
• fastText.py
• NLTK

Pretrained word vectors for fastText

Download a pretrained English word vectors. You can see the list of pretrained vectors on this page.

Datasets

• Oxford-102 flowers: images and captions
• Caltech-200 birds: images and captions

The caption data is from this repository.

Pretrained models

• Birds text embedding
• Flowers text embedding
• VGG-16
• Birds
• Flowers

Run

• scripts/train_text_embedding_[birds/flowers].sh
  Train a visual-semantic embedding model using the method of Kiros et al..
• scripts/train_[birds/flowers].sh
  Train a GAN using a pretrained text embedding model.
• scripts/test_[birds/flowers].sh
  Generate some examples using original images and semantically relevant texts.

Results

Acknowledgements

• Text to image synthesis
• StackGAN
• Semantic Image Synthesis via Adversarial Learning, implemented by Seonghyeon Nam

Citation

@inproceedings{duc2018Paired-D,
  author={Duc Minh Vo, Akihiro Sugimoto},
  title={Paired-D GAN for Semantic Image Synthesis},
  booktitle={ACCV},
  year={2018}
}