House-GAN++

Code and instructions for our paper: House-GAN++: Generative Adversarial Layout Refinement Network towards Intelligent Computational Agent for Professional Architects, CVPR 2021. Project website.

Data

We have used the RPLAN dataset, which offers 60k vector-graphics floorplans designed by professional architects. Qualitative and quantitative evaluations based on the three standard metrics (i.e., realism, diversity, and compatibility) in the literature demonstrate that the proposed system outperforms the current-state-of-the-art by a large margin.

Demo

Please check out our live demo.

Running pretrained models

See requirements.txt for checking the dependencies before running the code

For running a pretrained model check out the following steps:

• Run python test.py.
• Check out the results in output folder.

Training models

Coming Soon!

Citation

@misc{nauata2021housegan,
      title={House-GAN++: Generative Adversarial Layout Refinement Networks}, 
      author={Nelson Nauata and Sepidehsadat Hosseini and Kai-Hung Chang and Hang Chu and Chin-Yi Cheng and Yasutaka Furukawa},
      year={2021},
      eprint={2103.02574},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Contact

If you have any question, feel free to contact me at [email protected]

Acknowledgement

This research is partially supported by NSERC Discovery Grants, NSERC Discovery Grants Accelerator Supplements, DND/NSERC Discovery Grant Supplement, and Autodesk. We would like to thank architects and students for participating in our user study.