The Visual Computer

, Volume 35, Issue 6–8, pp 1157–1169 | Cite as

Stylistic scene enhancement GAN: mixed stylistic enhancement generation for 3D indoor scenes

  • Suiyun Zhang
  • Zhizhong Han
  • Yu-Kun Lai
  • Matthias Zwicker
  • Hui ZhangEmail author
Original Article


In this paper, we present stylistic scene enhancement GAN, SSE-GAN, a conditional Wasserstein GAN-based approach to automatic generation of mixed stylistic enhancements for 3D indoor scenes. An enhancement indicates factors that can influence the style of an indoor scene such as furniture colors and occurrence of small objects. To facilitate network training, we propose a novel enhancement feature encoding method, which represents an enhancement by a multi-one-hot vector, and effectively accommodates different enhancement factors. A Gumbel-Softmax module is introduced in the generator network to enable the generation of high fidelity enhancement features that can better confuse the discriminator. Experiments show that our approach is superior to the other baseline methods and successfully models the relationship between the style distribution and scene enhancements. Thus, although only trained with a dataset of room images in single styles, the trained generator can generate mixed stylistic enhancements by specifying multiple styles as the condition. Our approach is the first to apply a Gumbel-Softmax module in conditional Wasserstein GANs, as well as the first to explore the application of GAN-based models in the scene enhancement field.


Scene enhancement 3D indoor scenes Interior design Conditional generative adversarial nets Gumbel-Softmax Multi-one-hot 



This work was supported by the National Natural Science Foundation of China (61373070), NSF (1813583) and Tsinghua-Kuaishou Institute of Future Media Data.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Suiyun Zhang
    • 1
    • 2
  • Zhizhong Han
    • 3
  • Yu-Kun Lai
    • 4
  • Matthias Zwicker
    • 3
  • Hui Zhang
    • 1
    • 2
    Email author
  1. 1.School of SoftwareTsinghua UniversityBeijingChina
  2. 2.Beijing National Research Center for Information Science and Technology (BNRist)BeijingChina
  3. 3.Department of Computer ScienceUniversity of MarylandCollege ParkUnited States
  4. 4.School of Computer Science and InformaticsCardiff UniversityWalesUnited Kingdom

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