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arXiv · 2501.01197

LayeringDiff: Layered Image Synthesis via Generation, then Disassembly with Generative Knowledge

Kyoungkook Kang·Gyujin Sim·Geonung Kim·Donguk Kim·Seungho Nam·Sunghyun Cho
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Abstract

Layers have become indispensable tools for professional artists, allowing them to build a hierarchical structure that enables independent control over individual visual elements. In this paper, we propose LayeringDiff, a novel pipeline for the synthesis of layered images, which begins by generating a composite image using an off-the-shelf image generative model, followed by disassembling the image into its constituent foreground and background layers. By extracting layers from a composite image, rather than generating them from scratch, LayeringDiff bypasses the need for large-scale training to develop generative capabilities for individual layers. Furthermore, by utilizing a pretrained off-the-shelf generative model, our method can produce diverse contents and object scales in synthesized layers. For effective layer decomposition, we adapt a large-scale pretrained generative prior to estimate foreground and background layers. We also propose high-frequency alignment modules to refine the fine-details of the estimated layers. Our comprehensive experiments demonstrate that our approach effectively synthesizes layered images and supports various practical applications.

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Kyoungkook Kang, Gyujin Sim, Geonung Kim, Donguk Kim, Seungho Nam, Sunghyun Cho. 2025-01-02. LayeringDiff: Layered Image Synthesis via Generation, then Disassembly with Generative Knowledge. https://arxiv.org/abs/2501.01197

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