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

TL;DR

PosterLLaVa introduces a unified multi-modal layout generator leveraging large language models, achieving state-of-the-art results and enabling automated, flexible graphic design tasks including user-constrained poster creation.

Contribution

It presents a novel data-driven framework using structured text and visual instruction tuning for multi-modal layout generation, along with new challenging datasets and an automated poster system.

Findings

Achieved state-of-the-art performance on public benchmarks.

Developed two new datasets for complex design tasks.

Created an automated SVG poster generation system.

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.