aiqbench analysis
STEPS introduces a novel diffusion model architecture that significantly improves style preservation during visual text editing by decoupling font and color characteristics from textual content. This approach addresses a critical limitation in current generative models where altering text often degrades the original image's aesthetic consistency. The method represents a meaningful advancement for applications requiring high-fidelity text manipulation, such as automated document correction or synthetic data generation.
STEPS: Scene Text Editing with Preserved Style Using Diffusion and Contrastive Style Encoding
arXiv:2609.38636v1 Announce Type: cross Abstract: We introduce Scene Text Editing with Preserved Style (STEPS), a novel diffusion model architecture for quality text replacement in images. Scene Text Editing (STE), also known as Visual Text Editing, consists of changing the textual content in an image while conserving the original style, e.g. font, colors, orientation, background, etc. STEPS advances the state of the art in STE through directed focus on improved style preservation. We introduce a style encoder for visual text that captures style independently of textual content, and a model architecture that combines the style encoder with multiple semantic conditions (target text characters encoding and rendered glyphs). STEPS achieves superior results to previous STE methods in style preservation, output readability, and subjective quality.
Abstract:We introduce Scene Text Editing with Preserved Style (STEPS), a novel diffusion model architecture for quality text replacement in images. Scene Text Editing (STE), also known as Visual Text Editing, consists of changing the textual content in an image while conserving the original style, e.g. font, colors, orientation, background, etc. STEPS advances the state of the art in STE through directed focus on improved style preservation. We introduce a style encoder for visual text that captures style independently of textual content, and a model architecture that combines the style encoder with multiple semantic conditions (target text characters encoding and rendered glyphs). STEPS achieves superior results to previous STE methods in style preservation, output readability, and subjective quality.
9 pages, 5 figures, 4 tables. A version of this paper appeared in PAKDD 2026 (LNCS vol. 16618)
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
arXiv:2609.38636 [cs.CV]
(or arXiv:2609.38636v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.38636 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
Data Science: Foundations and Applications (PAKDD 2026), Lecture Notes in Computer Science, vol. 16618, pp. 473-484, Springer (2027)
https://doi.org/10.1007/978-981-92-1947-6_38 Focus to learn more DOI(s) linking to related resources
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