Graphic layout generation, a growing research field, plays a significant role
in user engagement and information perception. Existing methods primarily treat
layout generation as a numerical optimization task, focusing on quantitative
aspects while overlooking the semantic information of layout, such as the
relationship between each layout element. In this paper, we propose LayoutNUWA,
the first model that treats layout generation as a code generation task to
enhance semantic information and harness the hidden layout expertise of large
language models~(LLMs). More concretely, we develop a Code Instruct Tuning
(CIT) approach comprising three interconnected modules: 1) the Code
Initialization (CI) module quantifies the numerical conditions and initializes
them as HTML code with strategically placed masks; 2) the Code Completion (CC)
module employs the formatting knowledge of LLMs to fill in the masked portions
within the HTML code; 3) the Code Rendering (CR) module transforms the
completed code into the final layout output, ensuring a highly interpretable
and transparent layout generation procedure that directly maps code to a
visualized layout. We attain significant state-of-the-art performance (even
over 50\% improvements) on multiple datasets, showcasing the strong
capabilities of LayoutNUWA. Our code is available at
this https URL

借助大型语言模型，本文提出了 LayoutNUWA 模型，将布局生成任务作为代码生成任务来增强语义信息和利用隐藏的布局专长，达到显著的最新性能，并展示了 LayoutNUWA 的强大能力。

LayoutNUWA: 揭示大型语言模型的隐藏布局技能

LayoutNUWA: Revealing the Hidden Layout Expertise of Large Language  Models

In this paper, we study the graphic layout generation problem of producing
high-quality visual-textual presentation designs for given images. We note that
image compositions, which contain not only global semantics but also spatial
information, would largely affect layout results. Hence, we propose a deep
generative model, dubbed as composition-aware graphic layout GAN (CGL-GAN), to
synthesize layouts based on the global and spatial visual contents of input
images. To obtain training images from images that already contain manually
designed graphic layout data, previous work suggests masking design elements
(e.g., texts and embellishments) as model inputs, which inevitably leaves hint
of the ground truth. We study the misalignment between the training inputs
(with hint masks) and test inputs (without masks), and design a novel domain
alignment module (DAM) to narrow this gap. For training, we built a large-scale
layout dataset which consists of 60,548 advertising posters with annotated
layout information. To evaluate the generated layouts, we propose three novel
metrics according to aesthetic intuitions. Through both quantitative and
qualitative evaluations, we demonstrate that the proposed model can synthesize
high-quality graphic layouts according to image compositions.

本文提出了一种基于图像元素的深度生成模型 CGL-GAN，并设计了域对齐模块 DAM 来缩小训练和测试输入之间的差距，进而生成符合美感直觉的高质量图形布局。