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LayeringDiff: Layered Image Synthesis via Generation, then Disassembly with Generative Knowledge
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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.
Forward citations
Cited by 7 Pith papers
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LiWi: Layering in the Wild
LiWi uses an agent-driven data synthesis pipeline to build the LiWi-100k dataset and a model with shadow-guided and degradation-restoration objectives that achieves SoTA performance on RGB L1 and Alpha IoU for natural...
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LiWi: Layering in the Wild
Introduces LiWi-100k dataset via agent-orchestrated synthesis and a decomposition model with shadow-guided learning and boundary correction that claims state-of-the-art RGB L1 and Alpha IoU on natural images.
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A Unified and Controllable Framework for Layered Image Generation with Visual Effects
LASAGNA produces layered images with integrated visual effects in a single pass, enabling drift-free edits via alpha compositing while releasing a 48K dataset and a 242-sample benchmark.
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Stable-Layers: Fine-Tuning Image Layer Decomposition Models with VLM-Scored Reinforcement Learning
Stable-Layers applies Flow-GRPO with LoRA and a two-stage VLM scoring pipeline to improve layer decomposition without paired supervision, yielding stronger separation and lower reconstruction error on Crello.
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LiWi: Layering in the Wild
Presents LiWi-100k dataset generated via agent-driven decomposition and a model achieving SoTA on RGB L1 and Alpha IoU for natural image layering.
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LimeCross: Context-Conditioned Layered Image Editing with Structural Consistency
LimeCross enables text-guided editing of individual layers in composite images by conditioning on cross-layer context via bi-stream attention while preserving layer integrity and introducing the LayerEditBench benchmark.
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The chemical DNA of the Magellanic Clouds VI. Origin and evolution of neutron-capture elements in the SMC
SMC neutron-capture abundance patterns require both an enhanced delayed r-process at low metallicity and a top-lighter IMF relative to Kroupa (2001).
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