Content-aware visual-textual presentation layout aims at arranging spatial space on the given canvas for pre-defined elements, including text, logo, and underlay, which is a key to automatic template-free creative graphic design. In practical applications, e.g., poster designs, the canvas is originally non-empty, and both inter-element relationships as well as inter-layer relationships should be concerned when generating a proper layout. A few recent works deal with them simultaneously, but they still suffer from poor graphic performance, such as a lack of layout variety or spatial non-alignment. Since content-aware visual-textual presentation layout is a novel task, we first construct a new dataset named PosterLayout, which consists of 9,974 poster-layout pairs and 905 images, i.e., non-empty canvases. It is more challenging and useful for greater layout variety, domain diversity, and content diversity. Then, we propose design sequence formation (DSF) that reorganizes elements in layouts to imitate the design processes of human designers, and a novel CNN-LSTM-based conditional generative adversarial network (GAN) is presented to generate proper layouts. Specifically, the discriminator is design-sequence-aware and will supervise the "design" process of the generator. Experimental results verify the usefulness of the new benchmark and the effectiveness of the proposed approach, which achieves the best performance by generating suitable layouts for diverse canvases.

翻译：摘要：内容感知的视觉-文本展示布局旨在为预定义元素（包括文本、徽标和衬底）在给定画布上安排空间布局，这是实现自动化无模板创意图形设计的关键。在实际应用（如海报设计）中，画布最初并非空置，生成合理布局时需同时考虑元素间关系与图层间关系。近期虽有少数研究尝试同时处理这两类关系，但仍存在布局多样性不足或空间非对齐等图形表现欠佳的问题。由于内容感知的视觉-文本展示布局属于新兴任务，我们首先构建了一个名为PosterLayout的新数据集，包含9,974个海报-布局对和905张图像（即非空画布）。该数据集因具备更丰富的布局多样性、领域多样性和内容多样性而更具挑战性与实用性。随后，我们提出设计序列生成（DSF）方法，通过重组布局中的元素来模拟人类设计师的设计过程，并基于CNN-LSTM架构的条件生成对抗网络（GAN）生成合理布局。具体而言，判别器具备设计序列感知能力，可监督生成器的"设计"过程。实验结果验证了新基准的实用价值与所提方法的有效性——该方法能为多样化画布生成适配布局，取得最优性能。