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

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Abstract

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.

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Acknowledgements

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

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Authors and Affiliations

  1. School of Software, Tsinghua University, Beijing, China

    Suiyun Zhang & Hui Zhang

  2. Beijing National Research Center for Information Science and Technology (BNRist), Beijing, China

    Suiyun Zhang & Hui Zhang

  3. Department of Computer Science, University of Maryland, College Park, Maryland, MD 20742, United States

    Zhizhong Han & Matthias Zwicker

  4. School of Computer Science and Informatics, Cardiff University, Wales, United Kingdom

    Yu-Kun Lai

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  1. Suiyun Zhang
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  2. Zhizhong Han
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  3. Yu-Kun Lai
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  4. Matthias Zwicker
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  5. Hui Zhang
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Correspondence to Hui Zhang.

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Zhang, S., Han, Z., Lai, YK. et al. Stylistic scene enhancement GAN: mixed stylistic enhancement generation for 3D indoor scenes. Vis Comput 35, 1157–1169 (2019). https://doi.org/10.1007/s00371-019-01691-w

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