Abstract
This paper proposes a novel graph-constrained generative adversarial network, whose generator and discriminator are built upon relational architecture. The main idea is to encode the constraint into the graph structure of its relational networks. We have demonstrated the proposed architecture for a new house layout generation problem, whose task is to take an architectural constraint as a graph (i.e., the number and types of rooms with their spatial adjacency) and produce a set of axis-aligned bounding boxes of rooms. We measure the quality of generated house layouts with the three metrics: the realism, the diversity, and the compatibility with the input graph constraint. Our qualitative and quantitative evaluations over 117,000 real floorplan images demonstrate that the proposed approach outperforms existing methods and baselines. We will publicly share all our code and data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Room types are “living room”, “kitchen”, “bedroom”, “bathroom”, “closet”, “balcony”, “corridor”, “dining room”, “laundry room”, or “unkown”.
References
Lifull home’s dataset. https://www.nii.ac.jp/dsc/idr/lifull
Abu-Aisheh, Z., Raveaux, R., Ramel, J.Y., Martineau, P.: An exact graph edit distance algorithm for solving pattern recognition problems (2015)
Ashual, O., Wolf, L.: Specifying object attributes and relations in interactive scene generation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4561–4569 (2019)
Bao, F., Yan, D.M., Mitra, N.J., Wonka, P.: Generating and exploring good building layouts. ACM Trans. Graph. (TOG) 32(4), 1–10 (2013)
Choi, Y., et al.: StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8789–8797 (2018)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein gans. In: Advances in Neural Information Processing Systems, pp. 5767–5777 (2017)
Harada, M., Witkin, A., Baraff, D.: Interactive physically-based manipulation of discrete/continuous models. In: Proceedings of the 22nd Annual Conference on Computer Gand Interactive Techniques, pp. 199–208 (1995)
Hendrikx, M., Meijer, S., Van Der Velden, J., Iosup, A.: Procedural content generation for games: a survey. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 9(1), 1–22 (2013)
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: Advances in Neural Information Processing Systems, pp. 6626–6637 (2017)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
Johnson, J., Gupta, A., Fei-Fei, L.: Image generation from scene graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1219–1228 (2018)
Jyothi, A.A., Durand, T., He, J., Sigal, L., Mori, G.: LayoutVAE: stochastic scene layout generation from a label set. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9895–9904 (2019)
Karras, T., et al.: Analyzing and improving the image quality of styleGAN. arXiv preprint arXiv:1912.04958 (2019)
Kwon, Y.H., Park, M.G.: Predicting future frames using retrospective cycle GAN. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1811–1820 (2019)
Li, J., Yang, J., Hertzmann, A., Zhang, J., Xu, T.: LayoutGAN: generating graphic layouts with wireframe discriminators. arXiv preprint arXiv:1901.06767 (2019)
Liu, C., Wu, J., Kohli, P., Furukawa, Y.: Raster-to-vector: revisiting floorplan transformation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2195–2203 (2017)
Ma, C., Vining, N., Lefebvre, S., Sheffer, A.: Game level layout from design specification. Comput. Graph. Forum 33, 95–104 (2014). Wiley Online Library
Merrell, P., Schkufza, E., Koltun, V.: Computer-generated residential building layouts. ACM Trans. Graph. (TOG) 29, 181 (2010). ACM
Müller, P., Wonka, P., Haegler, S., Ulmer, A., Van Gool, L.: Procedural modeling of buildings. In: ACM SIGGRAPH 2006 Papers, pp. 614–623 (2006)
Peng, C.H., Yang, Y.L., Wonka, P.: Computing layouts with deformable templates. ACM Trans. Graph. (TOG) 33(4), 1–11 (2014)
Ritchie, D., Wang, K., Lin, Y.A.: Fast and flexible indoor scene synthesis via deep convolutional generative models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6182–6190 (2019)
Wang, K., Lin, Y.A., Weissmann, B., Savva, M., Chang, A.X., Ritchie, D.: Planit: planning and instantiating indoor scenes with relation graph and spatial prior networks. ACM Trans. Graph. (TOG) 38(4), 132 (2019)
Wang, K., Savva, M., Chang, A.X., Ritchie, D.: Deep convolutional priors for indoor scene synthesis. ACM Trans. Graph. (TOG) 37(4), 1–14 (2018)
Wu, W., Fu, X.M., Tang, R., Wang, Y., Qi, Y.H., Liu, L.: Data-driven interior plan generation for residential buildings. ACM Trans. Graph. (TOG) 38(6), 1–12 (2019)
Zhang, F., Nauata, N., Furukawa, Y.: Conv-MPN: convolutional message passing neural network for structured outdoor architecture reconstruction. arXiv preprint arXiv:1912.01756 (2019)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Acknowledgement
This research is partially supported by NSERC Discovery Grants, NSERC Discovery Grants Accelerator Supplements, and DND/NSERC Discovery Grant Supplement. We would like to thank architects and students for participating in our user study.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Nauata, N., Chang, KH., Cheng, CY., Mori, G., Furukawa, Y. (2020). House-GAN: Relational Generative Adversarial Networks for Graph-Constrained House Layout Generation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12346. Springer, Cham. https://doi.org/10.1007/978-3-030-58452-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-58452-8_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58451-1
Online ISBN: 978-3-030-58452-8
eBook Packages: Computer ScienceComputer Science (R0)