GLDesigner: Leveraging Multi-Modal LLMs as Designer for Enhanced Aesthetic Text Glyph Layouts

2024-11-18

arXiv.org

AI Summary

Text-based logo layout design has long lacked dedicated investigation, with existing methods largely subsumed under generic layout tasks. This paper introduces the first multimodal large vision-language model (VLM) framework tailored for glyph-level layout generation. Our approach comprises three core contributions: (1) a novel glyph-level instruction-tuning paradigm; (2) a large-scale dual-text logo dataset—five times larger than prior benchmarks—featuring fine-grained geometric annotations and natural-language layout descriptions; and (3) a lightweight multi-image encoder coupled with a geometry-semantic joint supervision mechanism enabling parallel processing of multiple glyphs. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art approaches on both geometric aesthetics metrics and human preference evaluations. Moreover, it achieves stable, controllable, and high-aesthetic-quality text logo generation under complex user-specified constraints.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Language and Vision

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Text logo design heavily relies on the creativity and expertise of professional designers, in which arranging element layouts is one of the most important procedures. However, few attention has been paid to this specific task which needs to take precise textural details and user constraints into consideration, but only on the broader tasks such as document/poster layout generation. In this paper, we propose a VLM-based framework that generates content-aware text logo layouts by integrating multi-modal inputs with user constraints, supporting a more flexible and stable layout design in real-world applications. We introduce two model techniques to reduce the computation for processing multiple glyph images simultaneously, while does not face performance degradation. To support instruction-tuning of out model, we construct two extensive text logo datasets, which are 5x more larger than the existing public dataset. Except for the geometric annotations (e.g. text masks and character recognition), we also compliment with comprehensive layout descriptions in natural language format, for more effective training to have reasoning ability when dealing with complex layouts and custom user constraints. Experimental studies demonstrate the effectiveness of our proposed model and datasets, when comparing with previous methods in various benchmarks to evaluate geometric aesthetics and human preferences. The code and datasets will be publicly available.

Problem

• Automating text logo layout design with multi-modal inputs
• Reducing computational cost for glyph image processing
• Enhancing layout generation with large-scale annotated datasets

Innovation

• VLM-based framework for text logo layouts
• Reduced computational cost techniques
• Extensive datasets with layout descriptions