Problem
Research questions and friction points this paper is trying to address.
text rendering
image generation
image editing
glyph priors
GlyphAnchor: Enhancing Visual Text Rendering via Position-Anchored Glyph Priors
🤖 AI Summary
针对图像生成和编辑模型中复杂文本渲染难题，提出GlyphAnchor方法，通过位置锚定的字形先验增强模型，并经阶段化监督微调及文本感知后训练提升效果。
📝 Abstract
Rendering accurate text remains difficult for image generation and editing models, especially when the target contains long, complex, and densely arranged text or rare characters. Existing approaches either improve native text rendering through stronger backbones and data-centric training without explicit glyph priors, or incorporate glyph priors through specialized designs that remain insufficiently accurate and robust under challenging scenarios. We introduce GlyphAnchor, a novel text-rendering enhancement method for both text-to-image and image-editing diffusion transformer models. GlyphAnchor enhances the backbone with lightweight glyph patch conditions whose positions are anchored to the target image through the model's native positional encoding. We train this capability with staged supervised finetuning and further refine it with text-aware post-training to improve robustness. We also introduce InfoTextBench, a benchmark for evaluating text-rich visual text rendering in both generation and editing settings. Experiments across multiple backbones and benchmarks, including long, complex, and densely arranged text and rare character scenarios, show that GlyphAnchor consistently improves text fidelity while preserving overall image quality.
Innovation
Methods, ideas, or system contributions that make the work stand out.
position-anchored glyph priors
lightweight glyph patch conditions
text-aware post-training
InfoTextBench
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