Illustrator's Depth: Monocular Layer Index Prediction for Image Decomposition
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917346514305024 |
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| author | Maruani, Nissim Zhang, Peiying Chaudhuri, Siddhartha Fisher, Matthew Zhao, Nanxuan Kim, Vladimir G. Alliez, Pierre Desbrun, Mathieu Yifan, Wang |
| author_facet | Maruani, Nissim Zhang, Peiying Chaudhuri, Siddhartha Fisher, Matthew Zhao, Nanxuan Kim, Vladimir G. Alliez, Pierre Desbrun, Mathieu Yifan, Wang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17454 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Illustrator's Depth: Monocular Layer Index Prediction for Image Decomposition Maruani, Nissim Zhang, Peiying Chaudhuri, Siddhartha Fisher, Matthew Zhao, Nanxuan Kim, Vladimir G. Alliez, Pierre Desbrun, Mathieu Yifan, Wang Computer Vision and Pattern Recognition 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. |
| title | Illustrator's Depth: Monocular Layer Index Prediction for Image Decomposition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.17454 |