Awesome
###<a href="http://www.scottreed.info/files/nips2016.pdf">Learning What and Where to Draw</a> Scott Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee
This is the code for our NIPS 2016 paper on text- and location-controllable image synthesis using conditional GANs. Much of the code is adapted from reedscot/icml2016 and dcgan.torch.
<img src="images/bbox_network.jpg" width="900px" height="220px"/> <img src="images/keypoint_network.jpg" width="900px" height="220px"/>####Setup Instructions
You will need to install Torch, CuDNN, stnbhwd and the display package.
####How to train a text to image model:
- Download the data including captions, location annotations and pretrained models.
- Download the birds and humans image data.
- Modify the
CONFIG
file to point to your data. - Run one of the training scripts, e.g.
./scripts/train_cub_keypoints.sh
####How to generate samples:
./scripts/run_all_demos.sh
.- html files will be generated with results like the following:
Moving the bird's position via bounding box:
<img src="images/cub_move_bbox.jpg" width="600px" height="300px"/>Moving the bird's position via keypoints:
<img src="images/cub_move_kp.jpg" width="600px" height="300px"/>Birds text to image with ground-truth keypoints:
<img src="images/cub_keypoints_given.jpg" width="600px" height="300px"/>Birds text to image with generated keypoints:
<img src="images/cub_keypoints_gen.jpg" width="600px" height="300px"/>Humans text to image with ground-truth keypoints:
<img src="images/mhp_kp_given.jpg" width="600px" height="300px"/>Humans text to image with generated keypoints:
<img src="images/mhp_kp_gen.jpg" width="600px" height="300px"/>####Citation
If you find this useful, please cite our work as follows:
@inproceedings{reed2016learning,
title={Learning What and Where to Draw},
author={Scott Reed and Zeynep Akata and Santosh Mohan and Samuel Tenka and Bernt Schiele and Honglak Lee},
booktitle={Advances in Neural Information Processing Systems},
year={2016}
}