You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This code is developed based on eyescream project with Torch: project site.
This code is the implementation of training and testing for S^2-GAN for the following paper:
Xiaolong Wang and Abhinav Gupta. Generative Image Modeling using Style and Structure Adversarial Networks. Proc. of European Conference on Computer Vision (ECCV), 2016. pdf
BibTeX:
@inproceedings{Wang_SSGAN2016,
Author = {Xiaolong Wang and Abhinav Gupta},
Title = {Generative Image Modeling using Style and Structure Adversarial Networks},
Booktitle = {ECCV},
Year = {2016},
}
Models and Datasets
The trained models can be downloaded from dropbox.
The pre-processed dataset (NYUv2) including rgb images and TV-denoised Surface Normals in jpgs can be downloaded from dropbox.
Update the path_dataset = '/scratch/xiaolonw/render_data/' in dataset.lua
Update the opt.save in train.lua for saving models
For testing, one can download the models into the ssgan_models folder.
Structure-GAN
The code for Stucture-GAN is in structure-gan:
train.lua: training Stucture-GAN
test.lua: testing Stucture-GAN
ssgan_models/Structure_GAN.net is our trained model
Style-GAN
The code for Style-GAN without FCN constraints is in style-gan-nofcn:
train.lua: training Style-GAN
test.lua: testing Style-GAN (To run this you need to download the dataset)
ssgan_models/Style_GAN_nofcn.net is our trained model
The code for Style-GAN with FCN constraints is in style-gan-fcn:
train_fcn.lua: training FCN for surface normal estimation
test_fcn.lua: testing FCN for surface normal estimation (To run this you need to download the dataset)
ssgan_models/FCN.net is our trained model
train_gan.lua: training Style-GAN
test_gan.lua: testing Style-GAN (To run this you need to download the dataset)
ssgan_models/joint_Style_GAN.net is our trained model
Joint Learning for S^2-GAN
The code for joint learning is in joint-ssgan:
train.lua: joint learning
test.lua: testing S^2-GAN
ssgan_models/joint_SSGAN.net is our trained model
