## 1 Introduction
### Literature Review  
AI can assist in architectural design and visualization by considering the patterns in previous design data to generate new designs. Deep learning algorithms have been used extensively in the architectural design and visualization domain with applications such as 2D and 3D generative architectural design, classification of architectural styles and building types, architectural drawings, and space recognition and indoor scene synthesis. There are relatively few studies on machine learning or deep learning applied to architectural design, and most of the main research objects are architectural design images. For example, [PERSON] et al. [1] built a GAN-based neural network to learn the house plan of a building, which generated the house plan layout within a specified boundary again. The authors trained two models with two types of household plan datasets from China and Japan, respectively. After comparing the results, the Chinese house plans were easier to understand, which also indicated that the Chinese house plans were somehow more systematic and templated. [PERSON] et al. [2] proposed an adaptive method to support the retrofitting of room configurations in frames during the early conceptual design phase. Their study proposed a method for the computer-aided generation of different floor plans during the early stages of conceptual design in architecture. The classification accuracy of the method reached about 93%. [PERSON] et al. [3] proposed the House-GAN algorithm to generate layouts of houses using an innovative graph-constrained GAN. The initial input to this algorithm is a bubble diagram specifying constraints such as the connectivity of rooms, number of rooms, and type of room. [PERSON] et al. [4] improved the conventional GAN and proposed a deep convolutional GAN (DCGAN) by including a set of constraints on the architecture of GAN and generating new bedroom designs. The use of DCGANs improved the stability of the generator training across a wide range of datasets and facilitated training with deeper networks and images with higher resolutions. This pix2 pix has a wide range of applications including generating photos provided by a sketch, generating color images from the input black and white pictures, and synthesizing photos from architectural labels. [PERSON] et al. [5] developed 'pix2 pix' software using a conditional generative adversarial network (cGAN), which was used to generate building facades using a given layout of the facade. [PERSON] et al. [6] proposed 'pix2 pixHD' software to extend conditional image generation to high-resolution images. A new objective function, a novel generator, and a new discriminator were proposed in pix2 pixHD, and the resolution of the generated images was enhanced to 2048 \(\times\) 1024 compared with the 256 \(\times\) 256 resolution in the original pix2 pix software. [PERSON] et al. [7] proposed an automatic extraction method for image design features based on deep learning. The ultimate goal was to develop an automatic extraction for these features and apply them to interior design, such as interior design case studies or interior element databases and recommendation systems. [PERSON] et al. [8] used the floor plan of a building as input data and the semantic segmentation results of various labeled elements as the output to obtain a GAN that could identify and label the elements in a building floor plan. Further studies, such as [PERSON] et al. [9], used vectorized coordinate values instead of pixel maps for the deep learning of a floor plan layout. The design elements in the house plan were vectorized into coordinate values to study a bedroom layout problem in the floor plan, and a neural network was used to generate a bedroom layout map. The vectorized data described the nature of CAD data more clearly and had stronger compatibility compared with pixelated images. In the realm of deep generative models, variational autoencoders are another type of algorithm that is widely used. VAEs are used both in 2D and 3D generative architectural designs.