This repository contains the code for the paper Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis.

To cite this work, please use

@inproceedings{Liao2020CVPR,
  title = {Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis},
  author = {Liao, Yiyi and Schwarz, Katja and Mescheder, Lars and Geiger, Andreas},
  booktitle = { Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020}
}

Our method requires an accessible CUDA device and is tested for Python 3.7.x .

Create and activate a conda environment with all requirements from the provided environment.yml file

conda env create -f environment.yml
conda activate controllable_gan

Build our customized version of Neural Mesh Renderer by running

cd externals/renderer/neural_renderer
python setup.py install

Here you can download the datasets used in our paper:

• Cars w/o background (0.5G)
• Cars with background (3.5G)
• Indoor (5.1G)

First download your data and put it into the ./data folder.

To train a new model, first create a config script similar to the ones provided in the ./configs folder. You can then train you model using

python train.py PATH_TO_CONFIG

To compute FID scores for your model use

python test.py PATH_TO_CONFIG

Finally, you can create nice samples with object rotation, translation and camera rotation using

python test_transforms.py PATH_TO_CONFIG

• Object rotation
  
• Object translation
  
• Camera rotation (azimuth)
  
• Camera rotation (polar)