PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLM
<|reference_start|>PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLM: Layout generation is the keystone in achieving automated graphic design, requiring arranging the position and size of various multi-modal design elements in a visually pleasing and constraint-following manner. Previous approaches are either inefficient for large-scale applications or lack flexibility for varying design requirements. Our research introduces a unified framework for automated graphic layout generation, leveraging the multi-modal large language model (MLLM) to accommodate diverse design tasks. In contrast, our data-driven method employs structured text (JSON format) and visual instruction tuning to generate layouts under specific visual and textual constraints, including user-defined natural language specifications. We conducted extensive experiments and achieved state-of-the-art (SOTA) performance on public multi-modal layout generation benchmarks, demonstrating the effectiveness of our method. Moreover, recognizing existing datasets' limitations in capturing the complexity of real-world graphic designs, we propose two new datasets for much more challenging tasks (user-constrained generation and complicated poster), further validating our model's utility in real-life settings. Marking by its superior accessibility and adaptability, this approach further automates large-scale graphic design tasks. The code and datasets will be publicly available on https://github.com/posterllava/PosterLLaVA.<|reference_end|>
@article{yang2024posterllava:,
    title={PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with
  LLM},
    author={Tao Yang, Yingmin Luo, Zhongang Qi, Yang Wu, Ying Shan, Chang Wen Chen},
    journal={arXiv preprint arXiv:2406.02884},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02884},
    primaryClass={cs.CV}
}