UniGlyph: Unified Segmentation-Conditioned Diffusion for Precise Visual Text Synthesis
Journal:
arXiv
Published Date:
Jul 1, 2025
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.
