LayerD: Decomposing Raster Graphic Designs into Layers

Tomoyuki Suzuki, Kang-Jun Liu, Naoto Inoue, Kota Yamaguchi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 17783-17792

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.