## 2 Methodology
### Architectural Appearance Generation Module  
When the architectural design textual descriptions have been translated into text semantic features by the DCE module, the new architectural design is performed by the AAG module, as shown in Figure 3. The DSTF-GAN of the AAG module translates the extracted design text semantics into high-rise conceptual sketches. Subsequently, the PR module performs recreation and generates realistic high-rise buildings. Figure 3: Overall architecture of the proposed AAG module. The DSTF-GAN is depicted in Figure 4, and the detailed properties of layers and operators are listed in Tables A1 and A2 in Appendix A. As noted by [PERSON] et al. [17], a simple GAN architecture is sufficient for sketch synthesis. Building on this, DSTF-GAN is designed to effectively integrate the inherent sparsity of sketches [17] and the extracted semantics of textual descriptions into the image features to generate high-quality sketches. As shown in Figure 4a, the generator of the DSTF-GAN introduces a novel block called the sparse text fusion (STF) block to seamlessly integrate both the sparsity of the sketches and text features into the generated sketches. The STF block consists of a sparse fusion (SF) block (Figure 5a) to incorporate the sparsity of sketches and a text fusion (TF) block (Figure 5b) to augment the feature maps with the conditioned text semantic features. Figure 4: Proposed DSTF-GAN architecture for text-to-conceptual sketch generation. \(z\) is a 200-dimensional latent code sampled from a normal distribution, and \(T\) is a 512-dimensional text semantic vector of textual description. FC, Conv, DeConv, BatchNorm, InstanceNorm, ReLU, LeakyReLU, and Tanh denote the fully connected layer, convolutional layer, deconvolutional layer, batch normalization layer, instance normalization layer, rectified linear unit activation function, LeakyReLU activation function, and hyperbolic tangent activation function, respectively. Additionally, the STF module, which fuses the sparsity and text features into sketches, refers to the sparse text fusion blocks. The detailed network architecture is shown in Table A1 in Appendix A. The SF block employs a two-headed neural network to extract the sparsity of sketches, transforming the latent vector \(\mathbf{z}\in\mathbb{R}^{200}\) into a channel sparsity coefficient \(\mathbf{\alpha}_{i}\in\mathbb{R}^{1\times 1\times C_{i}}\) and a position sparsity coefficient \(\mathbf{\beta}_{i}\in\mathbb{R}^{H_{i}\times W_{i}\times 1}\). Subsequently, the feature map \(\mathbf{h}_{i}\in\mathbb{R}^{H_{i}\times W_{i}\times C_{i}}\) is multiplied by two coefficients, and the results are summed to yield the output \(\mathbf{F}_{z,i}\in\mathbb{R}^{H_{i}\times W_{i}\times C_{i}}\). Sparsity fusion in the \(i\)-th STF block can be expressed as follows:  
\[\mathbf{h}_{i}=ReLU(f_{BN}(f_{D\text{Conv}}(\mathbf{F}_{i-1}))) \tag{11}\]  
\[\mathbf{\alpha}_{i}=ReLU\big{(}g_{i,1}^{2}(f_{i}^{2}(z))\big{)} \tag{12}\]  
\[\beta_{i}=ReLU\big{(}g_{i,2}^{z}\big{(}f_{reshape}(f_{i}^{z}(z))\big{)}\big{)} \tag{13}\]  
\[F_{z,i}^{j,k,l}=a_{i}^{l}\times h_{i}^{j,k,l}+\beta_{i}^{l}\times h_{i}^{j,k,l} \tag{14}\]  
where \(\mathbf{F}_{i-1}\in\mathbb{R}^{H_{i-1}\times W_{i-1}\times C_{i-1}}\) is the input feature maps of the \(i\)-th STF block; \(f_{D\text{Conv}}\), \(f_{BN}\), and \(ReLU\) denote the deconvolutional layer, the batch normalization layer, and the rectified linear unit activation function, respectively; \(f_{i}^{z},g_{i,1}^{z},\delta_{i,2}^{z}\) denotes an underlying shared layer (multi-layer perceptron, MLP) and two exclusive networks of the two-headed neural network, respectively; and \(f_{reshape}\) denotes the reshape operation, as shown in Figure 5a. The TF block employs two MLPs and a convolutional layer to predict a channel-wise coefficient \(\mathbf{w}_{i}\in\mathbb{R}^{1\times 1\times C_{i}}\) and a position-wise coefficient \(\mathbf{b}_{i}\in\mathbb{R}^{H_{i}\times W_{i}\times 1}\) based on the text feature \(\mathbf{T}\in\mathbb{R}^{512}\).