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首页> 外文期刊>The Visual Computer >Stylistic scene enhancement GAN: mixed stylistic enhancement generation for 3D indoor scenes
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Stylistic scene enhancement GAN: mixed stylistic enhancement generation for 3D indoor scenes

机译：风格现场增强GaN：3D室内场景混合的风格增强生成

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摘要

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.
机译：在本文中，我们呈现了文体现场增强GaN，SSE-GaN，一种条件Wasserstein GaN的方法，可以自动生成3D室内场景的混合风格增强。增强表明可以影响室内场景的风格，例如家具颜色和小物体的发生因素。为了促进网络培训，我们提出了一种新颖的增强特征编码方法，其表示多一热向量的增强，并有效地适应不同的增强因子。发电机网络中引入了Gumbel-SoftMax模块，以使得能够产生能够更好地混淆鉴别器的高保真增强功能。实验表明，我们的方法优于其他基线方法，并成功地模拟了风格分布和场景增强之间的关系。因此，尽管仅在单个样式中用房间图像的数据集接受训练，但是训练有素的发生器可以通过指定多种样式作为条件来产生混合的风格增强功能。我们的方法是第一个在有条件的Wassersein Gans应用Gumbel-Softmax模块，以及第一个探讨基于GaN的模型在场景增强字段中的应用。

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