From Elements to Design: A Layered Approach for Automatic Graphic Design Composition
Jiawei Lin, Shizhao Sun, Danqing Huang, Ting Liu, Ji Li, Jiang Bian
2024-12-30
Summary
This paper talks about LaDeCo, a new method for automatically creating graphic designs by organizing different design elements into layers, making the design process more efficient and effective.
What's the problem?
Many existing models for graphic design focus on specific tasks and do not consider the overall structure of a design. This can lead to incomplete or poorly organized designs. Additionally, these models often struggle with understanding how different elements should be combined to create a cohesive final product.
What's the solution?
To solve these problems, the authors introduce LaDeCo, which uses a layered approach to graphic design. This method first organizes input elements into different layers based on their meaning and content. Then, it predicts the attributes of each element in a step-by-step manner, incorporating previously created layers into the design process. By breaking down the complex task of graphic design into smaller, manageable steps, LaDeCo makes it easier to generate high-quality designs. The results show that LaDeCo performs well in various design tasks and can even adjust designs based on user needs.
Why it matters?
This research is important because it enhances how automatic graphic design tools work. By using a structured approach that considers the hierarchy of design elements, LaDeCo can improve the quality and efficiency of graphic design processes. This could lead to better tools for designers and more creative possibilities in fields like marketing, advertising, and digital content creation.
Abstract
In this work, we investigate automatic design composition from multimodal graphic elements. Although recent studies have developed various generative models for graphic design, they usually face the following limitations: they only focus on certain subtasks and are far from achieving the design composition task; they do not consider the hierarchical information of graphic designs during the generation process. To tackle these issues, we introduce the layered design principle into Large Multimodal Models (LMMs) and propose a novel approach, called LaDeCo, to accomplish this challenging task. Specifically, LaDeCo first performs layer planning for a given element set, dividing the input elements into different semantic layers according to their contents. Based on the planning results, it subsequently predicts element attributes that control the design composition in a layer-wise manner, and includes the rendered image of previously generated layers into the context. With this insightful design, LaDeCo decomposes the difficult task into smaller manageable steps, making the generation process smoother and clearer. The experimental results demonstrate the effectiveness of LaDeCo in design composition. Furthermore, we show that LaDeCo enables some interesting applications in graphic design, such as resolution adjustment, element filling, design variation, etc. In addition, it even outperforms the specialized models in some design subtasks without any task-specific training.