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arXiv 提交日期: 2026-04-08
📄 Abstract - Generative Phomosaic with Structure-Aligned and Personalized Diffusion

We present the first generative approach to photomosaic creation. Traditional photomosaic methods rely on a large number of tile images and color-based matching, which limits both diversity and structural consistency. Our generative photomosaic framework synthesizes tile images using diffusion-based generation conditioned on reference images. A low-frequency conditioned diffusion mechanism aligns global structure while preserving prompt-driven details. This generative formulation enables photomosaic composition that is both semantically expressive and structurally coherent, effectively overcoming the fundamental limitations of matching-based approaches. By leveraging few-shot personalized diffusion, our model is able to produce user-specific or stylistically consistent tiles without requiring an extensive collection of images.

顶级标签: computer vision aigc model training
详细标签: image generation diffusion models personalization photomosaic structure alignment 或 搜索:

结构对齐与个性化扩散的生成式照片马赛克 / Generative Phomosaic with Structure-Aligned and Personalized Diffusion


1️⃣ 一句话总结

这篇论文提出了一种全新的生成式照片马赛克方法,它利用扩散模型根据参考图像自动生成每一块小图,从而在保证整体结构一致性的同时,创造出语义丰富且风格统一的马赛克作品,克服了传统方法依赖海量图库和简单颜色匹配的局限。

源自 arXiv: 2604.06989