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.
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by Wanrong Zhu,... at arxiv.org 04-24-2024
https://arxiv.org/pdf/2404.15271.pdfDeeper Inquiries