UniGlyph: Unified Segmentation-Conditioned Diffusion for Precise Visual Text Synthesis

• URL: http://arxiv.org/abs/2507.00992v2
• Date: Wed, 02 Jul 2025 04:08:46 GMT
• Title: UniGlyph: Unified Segmentation-Conditioned Diffusion for Precise Visual Text Synthesis
• Authors: Yuanrui Wang, Cong Han, Yafei Li, Zhipeng Jin, Xiawei Li, SiNan Du, Wen Tao, Yi Yang, Shuanglong Li, Chun Yuan, Liu Lin,
• Abstract summary: We propose a segmentation-guided framework that uses pixel-level visual text masks as unified conditional inputs.<n>Our approach achieves state-of-the-art performance on the AnyText benchmark.<n>We also introduce two new benchmarks: GlyphMM-benchmark for testing layout and glyph consistency in complex, and MiniText-benchmark for assessing generation quality in small-scale text regions.
• Score: 38.658170067715965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-image generation has greatly advanced content creation, yet accurately rendering visual text remains a key challenge due to blurred glyphs, semantic drift, and limited style control. Existing methods often rely on pre-rendered glyph images as conditions, but these struggle to retain original font styles and color cues, necessitating complex multi-branch designs that increase model overhead and reduce flexibility. To address these issues, we propose a segmentation-guided framework that uses pixel-level visual text masks -- rich in glyph shape, color, and spatial detail -- as unified conditional inputs. Our method introduces two core components: (1) a fine-tuned bilingual segmentation model for precise text mask extraction, and (2) a streamlined diffusion model augmented with adaptive glyph conditioning and a region-specific loss to preserve textual fidelity in both content and style. Our approach achieves state-of-the-art performance on the AnyText benchmark, significantly surpassing prior methods in both Chinese and English settings. To enable more rigorous evaluation, we also introduce two new benchmarks: GlyphMM-benchmark for testing layout and glyph consistency in complex typesetting, and MiniText-benchmark for assessing generation quality in small-scale text regions. Experimental results show that our model outperforms existing methods by a large margin in both scenarios, particularly excelling at small text rendering and complex layout preservation, validating its strong generalization and deployment readiness.

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