{"ID":2838726,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.17454","arxiv_id":"2511.17454","title":"Illustrator's Depth: Monocular Layer Index Prediction for Image Decomposition","abstract":"We introduce Illustrator's Depth, a novel definition of depth that addresses a key challenge in digital content creation: decomposing flat images into editable, ordered layers. Inspired by an artist's compositional process, illustrator's depth infers a layer index to each pixel, forming an interpretable image decomposition through a discrete, globally consistent ordering of elements optimized for editability. We also propose and train a neural network using a curated dataset of layered vector graphics to predict layering directly from raster inputs. Our layer index inference unlocks a range of powerful downstream applications. In particular, it significantly outperforms state-of-the-art baselines for image vectorization while also enabling high-fidelity text-to-vector-graphics generation, automatic 3D relief generation from 2D images, and intuitive depth-aware editing. By reframing depth from a physical quantity to a creative abstraction, illustrator's depth prediction offers a new foundation for editable image decomposition.","short_abstract":"We introduce Illustrator's Depth, a novel definition of depth that addresses a key challenge in digital content creation: decomposing flat images into editable, ordered layers. Inspired by an artist's compositional process, illustrator's depth infers a layer index to each pixel, forming an interpretable image decomposi...","url_abs":"https://arxiv.org/abs/2511.17454","url_pdf":"https://arxiv.org/pdf/2511.17454v2","authors":"[\"Nissim Maruani\",\"Peiying Zhang\",\"Siddhartha Chaudhuri\",\"Matthew Fisher\",\"Nanxuan Zhao\",\"Vladimir G. Kim\",\"Pierre Alliez\",\"Mathieu Desbrun\",\"Wang Yifan\"]","published":"2025-11-21T17:56:43Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}