We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for the challenging graph-constrained house generation task. 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. Finally, we propose a novel graph-based cycle-consistency loss that aims at maintaining the relative spatial relationships between ground truth and predicted graphs. Experiments on two challenging graph-constrained house generation tasks (i.e., house layout and roof generation) with two public datasets demonstrate the effectiveness of GTGAN in terms of objective quantitative scores and subjective visual realism. New state-of-the-art results are established by large margins on both tasks.
翻译:我们展示了一个新型的图形变形变形格斗网络(GGGAN),目的是以端到端的方式学习具有挑战性、受图形限制的室内生成任务的有效图形节点关系。拟议的图形变形生成器包括一个新型的图形变形器编码器,该变形器将图形变形和自我关注结合在一个变形器中,以模拟连接和非连接的图形节点之间的本地和全球互动。具体地说,拟议的连接节点关注和非连接节点关注(NNA)旨在分别捕捉输入图中连接节点和非连接节点之间的全球关系。拟议的图形建模块(GMB)旨在利用基于房屋布局布局的本地顶点互动。此外,我们提出了一个新的基于节点的分类分析器,以维护不同房屋组件的高层静态和歧视性节点特征。最后,我们提出了一个新的基于图形的循环一致性损失,目的是维持地面和预测的图表间距之间的相对空间关系。两个具有挑战性的图表组合组合式的顶点 — 两个具有挑战性的顶部互动的图像模型实验,两个目标是通过内部的大型图像生成结果。 和图像生成的大规模数据格式格式,两个具有新的数字格式,用来展示。</s>