Core Concepts
Instruction-following models can be effectively applied to automate the layout planning of visually-rich documents, simplifying the design process for both professionals and non-professionals.
Abstract
This paper introduces DocLap, a novel method for solving the layout planning task for visually-rich documents using instruction-following models. The key highlights are:
The authors propose a multimodal instruction-following framework for layout planning, allowing users to easily arrange visual elements into tailored layouts by specifying canvas size and design purpose.
They developed three layout reasoning tasks - Coordinates Predicting, Layout Recovering, and Layout Planning - to train the model in understanding and executing layout instructions.
Experiments on two benchmark datasets, Crello and PosterLayout, show that DocLap not only simplifies the design process for non-professionals but also outperforms the performance of few-shot GPT-4V models, with mIoU higher by 12% on Crello.
The authors highlight the potential of multimodal instruction-following models to automate and simplify the design process, providing an approachable solution for a wide range of design tasks on visually-rich documents.
The paper also discusses the limitations of the current approach, such as the diminishing performance as the complexity of the layout increases, and the need for more comprehensive evaluation frameworks to capture the nuances of aesthetic and functional design quality.
Stats
The first figure is the background canvas of a design poster with a width of 128 and a height of 128.
The following images are a few text components or logos to be added to the poster.
The first figure is a Facebook AD with a width of 128 and a height of 128; and it composes of various components as listed in the following images.
The first figure is a design template with a width of 128 and a height of 128; and it composes of various components.
Quotes
"Recent advancements in large language models (LLMs) have showcased their remarkable ability to follow human instructions and execute specified tasks, introducing a new level of flexibility and control in human-computer interaction."
"Alongside these developments, we have witnessed the emergence of instruction-tuned multimodal models, extending the capabilities of LLMs to understand and process information across both textual and visual domains."