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

• URL: http://arxiv.org/abs/2411.11435v1
• Date: Mon, 18 Nov 2024 10:04:10 GMT
• Title: GLDesigner: Leveraging Multi-Modal LLMs as Designer for Enhanced Aesthetic Text Glyph Layouts
• Authors: Junwen He, Yifan Wang, Lijun Wang, Huchuan Lu, Jun-Yan He, Chenyang Li, Hanyuan Chen, Jin-Peng Lan, Bin Luo, Yifeng Geng,
• Abstract summary: We propose a VLM-based framework that generates content-aware text logo layouts. We introduce two model techniques to reduce the computation for processing multiple glyph images simultaneously. To support instruction-tuning of out model, we construct two extensive text logo datasets, which are 5x more larger than the existing public dataset.
• Score: 53.568057283934714
• License:
• 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.

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