SkyReels-Text: Fine-grained Font-Controllable Text Editing for Poster Design

• URL: http://arxiv.org/abs/2511.13285v1
• Date: Mon, 17 Nov 2025 12:02:52 GMT
• Title: SkyReels-Text: Fine-grained Font-Controllable Text Editing for Poster Design
• Authors: Yunjie Yu, Jingchen Wu, Junchen Zhu, Chunze Lin, Guibin Chen,
• Abstract summary: We present SkyReels-Text, a novel font-controllable framework for precise poster text editing.<n>Our method enables simultaneous editing of multiple text regions, each rendered in distinct styles.<n>SkyReels-Text achieves state-of-the-art performance in both text fidelity and visual realism.
• Score: 8.272505030643039
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artistic design such as poster design often demands rapid yet precise modification of textual content while preserving visual harmony and typographic intent, especially across diverse font styles. Although modern image editing models have grown increasingly powerful, they still fall short in fine-grained, font-aware text manipulation, limiting their utility in professional design workflows such as poster editing. To address this issue, we present SkyReels-Text, a novel font-controllable framework for precise poster text editing. Our method enables simultaneous editing of multiple text regions, each rendered in distinct typographic styles, while preserving the visual appearance of non-edited regions. Notably, our model requires neither font labels nor fine-tuning during inference: users can simply provide cropped glyph patches corresponding to their desired typography, even if the font is not included in any standard library. Extensive experiments on multiple datasets, including handwrittent text benchmarks, SkyReels-Text achieves state-of-the-art performance in both text fidelity and visual realism, offering unprecedented control over font families, and stylistic nuances. This work bridges the gap between general-purpose image editing and professional-grade typographic design.

Related papers

• Autoregressive Styled Text Image Generation, but Make it Reliable [51.09340470015673]
  This work is dedicated to developing strategies that reproduce the characteristics of a given writer, with promising results in terms of style fidelity and generalization achieved by the recently proposed Autoregressive Transformer paradigm for HTG.<n>In this work, we rethink the autoregressive by framing HTG as a multimodal prompt-conditioned generation task, tackling the content controllability issues by introducing special input tokens for better alignment with the visual ones.
  arXiv  Detail & Related papers  (2025-10-27T11:54:23Z)
• POSTA: A Go-to Framework for Customized Artistic Poster Generation [87.16343612086959]
  POSTA is a modular framework for customized artistic poster generation.<n>Background Diffusion creates a themed background based on user input.<n>Design MLLM then generates layout and typography elements that align with and complement the background style.<n>ArtText Diffusion applies additional stylization to key text elements.
  arXiv  Detail & Related papers  (2025-03-19T05:22:38Z)
• 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)
• ControlText: Unlocking Controllable Fonts in Multilingual Text Rendering without Font Annotations [10.511255146429805]
  This work demonstrates that diffusion models can achieve font-controllable multilingual text rendering using just raw images without font label annotations.<n>We propose a data-driven solution that integrates the conditional diffusion model with a text segmentation model.<n>The experiment provides a proof of concept of our algorithm in zero-shot text and font editing across diverse fonts and languages.
  arXiv  Detail & Related papers  (2025-02-16T05:30:18Z)
• Text-Driven Image Editing via Learnable Regions [74.45313434129005]
  We introduce a method for region-based image editing driven by textual prompts, without the need for user-provided masks or sketches. We show that this simple approach enables flexible editing that is compatible with current image generation models. Experiments demonstrate the competitive performance of our method in manipulating images with high fidelity and realism that correspond to the provided language descriptions.
  arXiv  Detail & Related papers  (2023-11-28T02:27:31Z)
• TextPainter: Multimodal Text Image Generation with Visual-harmony and Text-comprehension for Poster Design [50.8682912032406]
  This study introduces TextPainter, a novel multimodal approach to generate text images. TextPainter takes the global-local background image as a hint of style and guides the text image generation with visual harmony. We construct the PosterT80K dataset, consisting of about 80K posters annotated with sentence-level bounding boxes and text contents.
  arXiv  Detail & Related papers  (2023-08-09T06:59:29Z)
• FASTER: A Font-Agnostic Scene Text Editing and Rendering Framework [19.564048493848272]
  Scene Text Editing (STE) is a challenging research problem, that primarily aims towards modifying existing texts in an image. Existing style-transfer-based approaches have shown sub-par editing performance due to complex image backgrounds, diverse font attributes, and varying word lengths within the text. We propose a novel font-agnostic scene text editing and rendering framework, named FASTER, for simultaneously generating text in arbitrary styles and locations.
  arXiv  Detail & Related papers  (2023-08-05T15:54:06Z)
• Improving Diffusion Models for Scene Text Editing with Dual Encoders [44.12999932588205]
  Scene text editing is a challenging task that involves modifying or inserting specified texts in an image. Recent advances in diffusion models have shown promise in overcoming these limitations with text-conditional image editing. We propose DIFFSTE to improve pre-trained diffusion models with a dual encoder design.
  arXiv  Detail & Related papers  (2023-04-12T02:08:34Z)
• Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting [53.708523312636096]
  We present Imagen Editor, a cascaded diffusion model built, by fine-tuning on text-guided image inpainting. edits are faithful to the text prompts, which is accomplished by using object detectors to propose inpainting masks during training. To improve qualitative and quantitative evaluation, we introduce EditBench, a systematic benchmark for text-guided image inpainting.
  arXiv  Detail & Related papers  (2022-12-13T21:25:11Z)
• Prompt-to-Prompt Image Editing with Cross Attention Control [41.26939787978142]
  We present an intuitive prompt-to-prompt editing framework, where the edits are controlled by text only. We show our results over diverse images and prompts, demonstrating high-quality synthesis and fidelity to the edited prompts.
  arXiv  Detail & Related papers  (2022-08-02T17:55:41Z)
• GenText: Unsupervised Artistic Text Generation via Decoupled Font and Texture Manipulation [30.654807125764965]
  We propose a novel approach, namely GenText, to achieve general artistic text style transfer. Specifically, our work incorporates three different stages, stylization, destylization, and font transfer. Considering the difficult data acquisition of paired artistic text images, our model is designed under the unsupervised setting.
  arXiv  Detail & Related papers  (2022-07-20T04:42:47Z)

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.