Text-Conditioned Background Generation for Editable Multi-Layer Documents

• URL: http://arxiv.org/abs/2512.17151v1
• Date: Fri, 19 Dec 2025 01:10:24 GMT
• Title: Text-Conditioned Background Generation for Editable Multi-Layer Documents
• Authors: Taewon Kang, Joseph K J, Chris Tensmeyer, Jihyung Kil, Wanrong Zhu, Ming C. Lin, Vlad I. Morariu,
• Abstract summary: We present a framework for document-centric background generation with multi-page editing and thematic continuity.<n>Our training-free framework produces visually coherent, text-preserving documents, bridging generative modeling with natural design.
• Score: 32.896370365677136
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a framework for document-centric background generation with multi-page editing and thematic continuity. To ensure text regions remain readable, we employ a \emph{latent masking} formulation that softly attenuates updates in the diffusion space, inspired by smooth barrier functions in physics and numerical optimization. In addition, we introduce \emph{Automated Readability Optimization (ARO)}, which automatically places semi-transparent, rounded backing shapes behind text regions. ARO determines the minimal opacity needed to satisfy perceptual contrast standards (WCAG 2.2) relative to the underlying background, ensuring readability while maintaining aesthetic harmony without human intervention. Multi-page consistency is maintained through a summarization-and-instruction process, where each page is distilled into a compact representation that recursively guides subsequent generations. This design reflects how humans build continuity by retaining prior context, ensuring that visual motifs evolve coherently across an entire document. Our method further treats a document as a structured composition in which text, figures, and backgrounds are preserved or regenerated as separate layers, allowing targeted background editing without compromising readability. Finally, user-provided prompts allow stylistic adjustments in color and texture, balancing automated consistency with flexible customization. Our training-free framework produces visually coherent, text-preserving, and thematically aligned documents, bridging generative modeling with natural design workflows.

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