TextRefine: Improving Textual Fidelity, Spatial Placement, and Glyph Rendering for Text Editing in Product Posters

• URL: http://arxiv.org/abs/2608.19637v1
• Date: Thu, 20 Aug 2026 05:07:51 GMT
• Title: TextRefine: Improving Textual Fidelity, Spatial Placement, and Glyph Rendering for Text Editing in Product Posters
• Abstract summary: Text editing in product posters entails inserting new text or replacing existing text while preserving product appearance, background content, and global composition.<n>We introduce textbfTextRefine, a task-aligned post-training framework that combines supervised fine-tuning with operation-specific reward optimization to address these complementary failure modes.<n>For text insertion, our text-span-level reward jointly assesses semantic fidelity and target-span coverage, penalizes spatial conflicts with products and existing text, and employs a gated structural constraint to preserve non-text regions.<n>For text replacement, our glyph-level reward leverages the connectionist temporal classification (
• Score: 14.200794184177829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text editing in product posters entails inserting new text or replacing existing text while preserving product appearance, background content, and global composition. Despite recent progress in instruction-based image editing, general-purpose models remain unreliable in this setting: they often omit or incorrectly render the target text, place it over salient products or pre-existing content, and produce structurally distorted or visually inconsistent glyphs. We introduce \textbf{TextRefine}, a task-aligned post-training framework that combines supervised fine-tuning with operation-specific reward optimization to address these complementary failure modes. For text insertion, our text-span-level reward jointly assesses semantic fidelity and target-span coverage, penalizes spatial conflicts with products and existing text, and employs a gated structural constraint to preserve non-text regions. For text replacement, our glyph-level reward leverages the connectionist temporal classification (CTC) posterior of the target character to provide graded supervision for fine-grained defects, including missing strokes, structural deformations, and confusion among visually similar characters. We further introduce \textbf{OpenTextEdit}, a dataset comprising 100K images for text editing in product posters, with multi-text layouts, detailed text attributes, product masks, and challenging low-frequency characters. Extensive experiments on both insertion and replacement demonstrate that TextRefine consistently outperforms the evaluated image editing baselines in textual fidelity, placement reliability, and glyph quality while better preserving source-image content.

Related papers

• GlyphAnchor: Enhancing Visual Text Rendering via Position-Anchored Glyph Priors [44.606371845449424]
  We introduce GlyphAnchor, a novel text-rendering enhancement method for both text-to-image and image-editing diffusion transformer models.<n> 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.
  arXiv  Detail & Related papers  (2026-09-02T09:23:23Z)
• TextWand: A Unified Framework for Scene Text Editing [32.59846554728947]
  TextWand is a framework that unifies scene text removal, generation, and replacement into a single model.<n>It achieves precise control over both text appearance and background integrity.<n>It delivers superior text content accuracy, layout and style consistency, and overall image quality across scene text removal, generation and replacement tasks.
  arXiv  Detail & Related papers  (2026-06-04T05:43:24Z)
• TextSculptor: Training and Benchmarking Scene Text Editing [88.11688559021628]
  We present TextSculptor, a comprehensive framework for data construction and evaluation of scene text editing.<n>TextSculptor improves open-source text editing performance and narrows the gap to proprietary models.
  arXiv  Detail & Related papers  (2026-05-20T12:22:26Z)
• Training-Free Occluded Text Rendering via Glyph Priors and Attention-Guided Semantic Blending [0.0]
  We present a training-free framework for occluded text rendering with a pretrained FLUX.1-dev backbone.<n>The task requires a model to render recognizable typography and place an occluding object over the intended text region.<n>We propose a restarted dual-stream inference framework that decouples text- preservation from occluder insertion.
  arXiv  Detail & Related papers  (2026-05-16T04:58:07Z)
• WeEdit: A Dataset, Benchmark and Glyph-Guided Framework for Text-centric Image Editing [103.68419705566146]
  We present WeEdit, a systematic solution encompassing a scalable data construction pipeline, two benchmarks, and a tailored two-stage training strategy.<n>Specifically, we propose a novel HTML-based automatic editing pipeline, which generates 330K training pairs covering diverse editing operations and 15 languages.<n>On the algorithmic side, we employ glyph-guided supervised fine-tuning to inject explicit spatial and content priors, followed by a multi-objective reinforcement learning stage to align generation with instruction adherence, text clarity, and background preservation.
  arXiv  Detail & Related papers  (2026-03-12T06:25:09Z)
• TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering [76.53315206999231]
  TextPecker is a plug-and-play structural anomaly perceptive RL strategy.<n>It mitigates noisy reward signals and works with any textto-image generators.<n>It significantly yields average gains of 4% in structural fidelity and 8.7% in semantic alignment for Chinese text rendering.
  arXiv  Detail & Related papers  (2026-02-24T13:40:23Z)
• TextGuider: Training-Free Guidance for Text Rendering via Attention Alignment [68.91073792449201]
  We propose TextGuider, a training-free method that encourages accurate and complete text appearance.<n>Specifically, we analyze attention patterns in Multi-Modal Diffusion Transformer(MM-DiT) models, particularly for text-related tokens intended to be rendered in the image.<n>Our method achieves state-of-the-art performance in test-time text rendering, with significant gains in recall and strong results in OCR accuracy and CLIP score.
  arXiv  Detail & Related papers  (2025-12-10T06:18:30Z)
• RepText: Rendering Visual Text via Replicating [15.476598851383919]
  We present RepText, which aims to empower pre-trained monolingual text-to-image generation models with the ability to accurately render visual text in user-specified fonts.<n>Specifically, we adopt the setting from ControlNet and additionally integrate language agnostic glyph and position of rendered text to enable generating harmonized visual text.<n>Our approach outperforms existing open-source methods and achieves comparable results to native multi-language closed-source models.
  arXiv  Detail & Related papers  (2025-04-28T12:19:53Z)
• DesignDiffusion: High-Quality Text-to-Design Image Generation with Diffusion Models [115.62816053600085]
  We present DesignDiffusion, a framework for synthesizing design images from textual descriptions.<n>The proposed framework directly synthesizes textual and visual design elements from user prompts.<n>It utilizes a distinctive character embedding derived from the visual text to enhance the input prompt.
  arXiv  Detail & Related papers  (2025-03-03T15:22:57Z)
• Beyond Flat Text: Dual Self-inherited Guidance for Visual Text Generation [17.552733309504486]
  In real-world images, slanted or curved texts, especially those on cans, banners, or badges, appear as frequently as flat texts due to artistic design or layout constraints.<n>We introduce a new training-free framework, STGen, which accurately generates visual texts in challenging scenarios.
  arXiv  Detail & Related papers  (2025-01-10T11:44:59Z)
• TextDiffuser: Diffusion Models as Text Painters [118.30923824681642]
  We introduce TextDiffuser, focusing on generating images with visually appealing text that is coherent with backgrounds. We contribute the first large-scale text images dataset with OCR annotations, MARIO-10M, containing 10 million image-text pairs. We show that TextDiffuser is flexible and controllable to create high-quality text images using text prompts alone or together with text template images, and conduct text inpainting to reconstruct incomplete images with text.
  arXiv  Detail & Related papers  (2023-05-18T10:16:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.