LayerD: Decomposing Raster Graphic Designs into Layers

Tomoyuki Suzuki, Kang-Jun Liu, Naoto Inoue, Kota Yamaguchi
9/29/2025
cs.GRcs.CV

No official code found. Best implementation: CyberAgentAILab/LayerD (137 stars, Apache-2.0, last commit 2026-01-19).

Abstract

Designers craft and edit graphic designs in a layer representation, but layer-based editing becomes impossible once composited into a raster image. In this work, we propose LayerD, a method to decompose raster graphic designs into layers for re-editable creative workflow. LayerD addresses the decomposition task by iteratively extracting unoccluded foreground layers. We propose a simple yet effective refinement approach taking advantage of the assumption that layers often exhibit uniform appearance in graphic designs. As decomposition is ill-posed and the ground-truth layer structure may not be reliable, we develop a quality metric that addresses the difficulty. In experiments, we show that LayerD successfully achieves high-quality decomposition and outperforms baselines. We also demonstrate the use of LayerD with state-of-the-art image generators and layer-based editing.

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Code Implementations(1)

Apache-2.0

[ICCV 2025] LayerD: Decomposing Raster Graphic Designs into Layers

13714Shell, PythonSep 26, 20259 months agoApache-2.0
computer-visiongraphic-designiccv2025layer-decompositionpsd+1 more

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Cite this paper

@article{suzuki2025layerd,
  title  = {LayerD: Decomposing Raster Graphic Designs into Layers},
  author = {Tomoyuki Suzuki and Kang-Jun Liu and Naoto Inoue and Kota Yamaguchi},
  year   = {2025},
  eprint = {2509.25134},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.25134v1}
}

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