PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLM

• URL: http://arxiv.org/abs/2406.02884v3
• Date: Tue, 26 Nov 2024 06:29:12 GMT
• Title: PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLM
• Authors: Tao Yang, Yingmin Luo, Zhongang Qi, Yang Wu, Ying Shan, Chang Wen Chen,
• Abstract summary: Our research introduces a unified framework for automated graphic layout generation. Our data-driven method employs structured text (JSON format) and visual instruction tuning to generate layouts. We develop an automated text-to-poster system that generates editable posters based on users' design intentions.
• Score: 58.67882997399021
• License:
• Abstract: 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. Finally, we develop an automated text-to-poster system that generates editable SVG posters based on users' design intentions, bridging the gap between layout generation and real-world graphic design applications. This system integrates our proposed layout generation method as the core component, demonstrating its effectiveness in practical scenarios. The code and datasets are open-sourced on https://github.com/posterllava/PosterLLaVA.

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