English

LayerD: Decomposing Raster Graphic Designs into Layers

Graphics 2025-09-30 v1 Computer Vision and Pattern Recognition

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.

Keywords

Cite

@article{arxiv.2509.25134,
  title  = {LayerD: Decomposing Raster Graphic Designs into Layers},
  author = {Tomoyuki Suzuki and Kang-Jun Liu and Naoto Inoue and Kota Yamaguchi},
  journal= {arXiv preprint arXiv:2509.25134},
  year   = {2025}
}

Comments

ICCV 2025, Project page: https://cyberagentailab.github.io/LayerD/ , GitHub: https://github.com/CyberAgentAILab/LayerD

R2 v1 2026-07-01T06:05:22.390Z