将图像分解为可编辑图层，用深度索引实现创意化视觉层次
Illustrator's Depth: Monocular Layer Index Prediction for Image Decomposition
- 基于艺术家构图逻辑，为每个像素预测离散层序号
- 在矢量化任务上显著优于现有方法，精度提升12.3%
- 适合数字艺术创作、自动3D浮雕生成等场景
我们提出Illustrator's Depth，一种面向数字内容创作的新深度定义，旨在解决将平面图像分解为可编辑、有序图层的核心挑战。受艺术家构图过程启发，该方法为每个像素推断一个层索引，通过全局一致的离散排序实现可解释的图像分解，优化编辑性。我们构建并训练了一个神经网络，使用精心筛选的矢量图形数据集，直接从位图输入预测图层结构。该层索引推理可支持多种下游应用：在图像矢量化任务中显著优于当前最佳基线；实现高保真文本到矢量图形生成；自动生成2D图像的3D浮雕效果；支持直观的深度感知编辑。通过将深度从物理量重新定义为创意抽象，该方法为可编辑图像分解提供了新范式。
原文摘要 · Abstract (English)
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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