Illustrator's Depth: Monocular Layer Index Prediction for Image Decomposition

• URL: http://arxiv.org/abs/2511.17454v1
• Date: Fri, 21 Nov 2025 17:56:43 GMT
• Title: Illustrator's Depth: Monocular Layer Index Prediction for Image Decomposition
• Authors: Nissim Maruani, Peiying Zhang, Siddhartha Chaudhuri, Matthew Fisher, Nanxuan Zhao, Vladimir G. Kim, Pierre Alliez, Mathieu Desbrun, Wang Yifan,
• Abstract summary: 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.<n>Inspired by an artist's compositional process, illustrator's depth infers a layer index to each pixel, forming an interpretable image decomposition.
• Score: 55.8308608221966
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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