## 2 Basic Theory
### Depth Extraction GAN  
Depth extraction GAN consists of three sub-networks: multi-feature style encoder, style transfer network, and discrimination network. Among them, style encoder and style transfer network are used to form image generator \(G\). The discrimination network is used as a discriminator to distinguish whether the generated image and style image are in the same style. Given content image \(x\in X\), image content is provided; style image \(y^{c}\in Y\) provides the style, and \(y^{c}\) is an image with the \(c\)th kind of style class extracted from \(N\) style images. Generator \(G\) in DE-GAN uses content image \(x\) to generate image \(\widetilde{x}^{c}\). Discriminator \(D\) guarantees whether \(\widetilde{x}^{c}\) is consistent with the style image, and its network structure is shown in Figure 1.  
In order to assemble the contribution of the four kinds of features to the image style, four feature extractors are used to extract shape feature \(F_{s}\), texture feature \(F_{t}\), color feature \(F_{c}\), and spatial depth \(F_{d}\) of the style image. Image \(x_{s}\in R^{3\times H\times W}\) provides the style as the input of the multi-feature extractor, and the multi-feature extractor outputs are multi-meta features \(F_{m}=\{x_{s},\,F_{s},\,F_{t},\,F_{c},\,F_{d}\}\in R^{C\times H\times W}\). The structure of the input style extractor is shown in Figure 2.  
Shape feature extractor. The semantic segmentation model can quickly extract multi-object objects from the image and output the mask information of multiple objects. Due to the lack of effective panoramic semantic annotation for image datasets, the shape feature extractor is applied to a pretrained U-net model [18]. The output of the shape feature extractor is shape feature \(F_{s}\in R^{H\times W}\). The value of \(F_{s}\) corresponds to the category result of each pixel with semantic segmentation. The network structure of the shape feature extractor is shown in Figure 3.  
Although we use U-net as the shape feature extractor, it gives no importance to the correctness of the category of the object corresponding to the label in the mask, because the shape feature extractor plays a semi-supervised learning role here. The most valuable information in \(F_{s}\) refers to the segmentation boundaries of different objects. Texture feature extractor. The shape special texture extractor can extract texture feature \(F_{t}\) from the original image, \(y^{t}\). The texture feature is mixed with discontinuous color information, including the three channels of RGB, so \(F_{s}\in R^{3\times H\times W}\). In order to avoid the interference of shape features and depth features, the object in the texture extractor should be different from the original image [19]. The network structure of the texture feature extractor is shown in Figure 4. In the texture feature extractor, \(G^{i}\) is the loss value of texture features in different dimensions. If the weighted loss of each layer \(G^{i}\) is minimized, the output texture map can better express the original image texture. Figure 1: Structure of DE-GAN. Figure 2: Structure of multi-feature extractor. Color feature extractor. Color is the visual experience resulting from the mixing of photons at different frequencies in the range of visible light. In digital images, the distribution of color can be represented by the distribution of pixel values of the three RGB channels within the range of [0, 255]. The most common feature representation method is the color histogram, but it is difficult to keep the same feature shape using color histograms in multi-feature extractors. Therefore, in the color extractor with different pixels, the fast Fourier transform is used to calculate the color histogram to calculate the color distribution of the style image, and the kurtosis maps of the three color channels are calculated as color features \(F_{c}\in\mathbb{R}^{H\times W\times 3}\). Spatial feature extractor.