用结构序列化与解耦，让模型按需生成有层次的版面。
StructLayoutFormer:Conditional Structured Layout Generation via Structure Serialization and Disentanglement
- 将版面结构转为序列，用Transformer生成带结构的布局。
- 首次实现条件化结构布局生成，生成结构更真实可控。
- 适合需要可编辑版面的UI/网页设计场景。
结构化版面在许多二维视觉内容（如GUI、网页）中更优，因其结构信息便于编辑。现有计算框架虽能生成版面，但依赖大量人工输入。当前数据驱动方法可自动创建固定版面，却无法生成结构。本文提出StructLayoutFormer，一种基于Transformer的条件化结构布局生成新方法。通过结构序列化将结构化版面表示为序列，并解耦结构信息与元素位置，以更好控制生成结构。本方法是首个实现条件化结构布局生成的数据驱动方案，能显式生成真实布局结构。实验表明，在引入后处理提取结构的情况下，该方法优于现有基线。同时验证了其在结构提取与迁移上的有效性。代码已公开于https://github.com/Teagrus/StructLayoutFormer。
原文摘要 · Abstract (English)
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}.
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