  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.
