Structured layouts are preferable in many 2D visual contents (\eg, GUIs, webpages) since the structural information allows convenient layout editing. Computational frameworks can help create structured layouts but require heavy labor input. Existing data-driven approaches are effective in automatically generating fixed layouts but fail to produce layout structures. We present StructLayoutFormer, a novel Transformer-based approach for conditional structured layout generation. We use a structure serialization scheme to represent structured layouts as sequences. To better control the structures of generated layouts, we disentangle the structural information from the element placements. Our approach is the first data-driven approach that achieves conditional structured layout generation and produces realistic layout structures explicitly. We compare our approach with existing data-driven layout generation approaches by including post-processing for structure extraction. Extensive experiments have shown that our approach exceeds these baselines in conditional structured layout generation. We also demonstrate that our approach is effective in extracting and transferring layout structures. The code is publicly available at %\href{https://github.com/Teagrus/StructLayoutFormer} {https://github.com/Teagrus/StructLayoutFormer}.

翻译：结构化布局在许多二维视觉内容（例如图形用户界面、网页）中更为可取，因为结构信息便于布局编辑。计算框架虽能辅助创建结构化布局，但需要大量人工投入。现有数据驱动方法能有效自动生成固定布局，但无法产生布局结构。本文提出StructLayoutFormer，一种基于Transformer的新型条件化结构化布局生成方法。我们采用结构序列化方案将结构化布局表示为序列。为了更好地控制生成布局的结构，我们将结构信息与元素排布进行解耦。本方法是首个实现条件化结构化布局生成并显式生成逼真布局结构的数据驱动方法。我们通过引入结构提取后处理，将本方法与现有数据驱动布局生成方法进行比较。大量实验表明，本方法在条件化结构化布局生成任务上超越这些基线方法。我们还验证了本方法在提取与迁移布局结构方面的有效性。代码已公开于https://github.com/Teagrus/StructLayoutFormer。