We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for challenging graph-constrained architectural layout generation tasks. The proposed graph-Transformer-based generator includes a novel graph Transformer encoder that combines graph convolutions and self-attentions in a Transformer to model both local and global interactions across connected and non-connected graph nodes. Specifically, the proposed connected node attention (CNA) and non-connected node attention (NNA) aim to capture the global relations across connected nodes and non-connected nodes in the input graph, respectively. The proposed graph modeling block (GMB) aims to exploit local vertex interactions based on a house layout topology. Moreover, we propose a new node classification-based discriminator to preserve the high-level semantic and discriminative node features for different house components. To maintain the relative spatial relationships between ground truth and predicted graphs, we also propose a novel graph-based cycle-consistency loss. Finally, we propose a novel self-guided pre-training method for graph representation learning. This approach involves simultaneous masking of nodes and edges at an elevated mask ratio (i.e., 40%) and their subsequent reconstruction using an asymmetric graph-centric autoencoder architecture. This method markedly improves the model's learning proficiency and expediency. Experiments on three challenging graph-constrained architectural layout generation tasks (i.e., house layout generation, house roof generation, and building layout generation) with three public datasets demonstrate the effectiveness of the proposed method in terms of objective quantitative scores and subjective visual realism. New state-of-the-art results are established by large margins on these three tasks.

翻译：我们提出了一种新颖的图Transformer生成对抗网络（GTGAN），以端到端方式学习有效的图节点关系，用于具有挑战性的图约束建筑布局生成任务。该基于图Transformer的生成器包含一个新颖的图Transformer编码器，该编码器在Transformer中结合了图卷积和自注意力机制，以建模连接和非连接图节点之间的局部与全局交互。具体而言，所提出的连接节点注意力（CNA）和非连接节点注意力（NNA）分别旨在捕捉输入图中连接节点与非连接节点之间的全局关系。所提出的图建模模块（GMB）旨在基于房屋布局拓扑结构利用局部顶点交互。此外，我们提出了一种新的基于节点分类的判别器，以保留不同房屋组件的高级语义和判别性节点特征。为了保持真实图和预测图之间的相对空间关系，我们还提出了一种新颖的基于图的循环一致性损失。最后，我们提出了一种用于图表示学习的自引导预训练方法。该方法涉及以高掩码率（即40%）同时掩蔽节点和边，随后使用非对称的图中心自编码器架构进行重建，显著提升了模型的学习能力和效率。在三个具有挑战性的图约束建筑布局生成任务（即房屋布局生成、房屋屋顶生成和建筑布局生成）上，使用三个公开数据集进行的实验表明，所提方法在客观定量评分和主观视觉真实感方面均具有有效性。在这三个任务上，我们以较大幅度建立了新的最优结果。