Automatic Layout Planning for Visually-Rich Documents with Instruction-Following Models
<|reference_start|>Automatic Layout Planning for Visually-Rich Documents with Instruction-Following Models: Recent advancements in instruction-following models have made user interactions with models more user-friendly and efficient, broadening their applicability. In graphic design, non-professional users often struggle to create visually appealing layouts due to limited skills and resources. In this work, we introduce a novel multimodal instruction-following framework for layout planning, allowing users to easily arrange visual elements into tailored layouts by specifying canvas size and design purpose, such as for book covers, posters, brochures, or menus. We developed three layout reasoning tasks to train the model in understanding and executing layout instructions. Experiments on two benchmarks show that our method not only simplifies the design process for non-professionals but also surpasses the performance of few-shot GPT-4V models, with mIoU higher by 12% on Crello. This progress highlights 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.<|reference_end|>
@article{zhu2024automatic,
    title={Automatic Layout Planning for Visually-Rich Documents with
  Instruction-Following Models},
    author={Wanrong Zhu, Jennifer Healey, Ruiyi Zhang, William Yang Wang, Tong Sun},
    journal={arXiv preprint arXiv:2404.15271},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15271},
    primaryClass={cs.CV cs.AI cs.CL}
}