LayerD: Decomposing Raster Graphic Designs into Layers

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. Code and models are publicly available.

Cite

Text

Suzuki et al. "LayerD: Decomposing Raster Graphic Designs into Layers." International Conference on Computer Vision, 2025.

Markdown

[Suzuki et al. "LayerD: Decomposing Raster Graphic Designs into Layers." International Conference on Computer Vision, 2025.](https://mlanthology.org/iccv/2025/suzuki2025iccv-layerd/)

BibTeX

@inproceedings{suzuki2025iccv-layerd,
  title     = {{LayerD: Decomposing Raster Graphic Designs into Layers}},
  author    = {Suzuki, Tomoyuki and Liu, Kang-Jun and Inoue, Naoto and Yamaguchi, Kota},
  booktitle = {International Conference on Computer Vision},
  year      = {2025},
  pages     = {17783-17792},
  url       = {https://mlanthology.org/iccv/2025/suzuki2025iccv-layerd/}
}