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

• URL: http://arxiv.org/abs/2501.01197v1
• Date: Thu, 02 Jan 2025 11:18:25 GMT
• Title: LayeringDiff: Layered Image Synthesis via Generation, then Disassembly with Generative Knowledge
• Authors: Kyoungkook Kang, Gyujin Sim, Geonung Kim, Donguk Kim, Seungho Nam, Sunghyun Cho,
• Abstract summary: LayeringDiff is a novel pipeline for the synthesis of layered images.<n>By extracting layers from a composite image, rather than generating them from scratch, LayeringDiff bypasses the need for large-scale training.<n>For effective layer decomposition, we adapt a large-scale pretrained generative prior to estimate foreground and background layers.
• Score: 14.481577976493236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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