This is a Tensorflow implementation of cascaded refinement networks to synthesize photographic images from semantic layouts.
Required python libraries: Tensorflow (>=1.0) + Scipy + Numpy + Pillow.
Tested in Ubuntu + Intel i7 CPU + Nvidia Titan X (Pascal) with Cuda (>=8.0) and CuDNN (>=5.0). CPU mode should also work with minor changes.
To train a model at 256p resolution, please set "is_training=True" and change the file paths for training and test sets accordingly in "demo_256p.py". Then run "demo_256p.py".
To train a model at 512p resolution, we fine-tune the pretrained model at 256p using "demo_512p.py". Also change "is_training=True" and file paths accordingly.
To train a model at 1024p resolution, we fine-tune the pretrained model at 512p using "demo_1024p.py". Also change "is_training=True" and file paths accordingly.
If you use our code for research, please cite our paper:
Qifeng Chen and Vladlen Koltun. Photographic Image Synthesis with Cascaded Refinement Networks. In ICCV 2017.
The scripts are put in the folder "mturk_scripts".
If you have any question or request about the code and data, please email me at chenqifeng22@gmail.com. If you need the pretrained model on NYU, please send an email to me.
MIT License