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.

翻译：我们提出了插画师深度，这是一种新颖的深度定义，旨在解决数字内容创作中的一个关键挑战：将平面图像分解为可编辑、有序的图层。受艺术家构图过程的启发，插画师深度为每个像素推断一个图层索引，通过离散且全局一致的元素排序形成可解释的图像分解，该排序针对可编辑性进行了优化。我们还提出并训练了一个神经网络，使用精心策划的分层矢量图形数据集，直接从栅格输入预测图层结构。我们的图层索引推断解锁了一系列强大的下游应用。具体而言，它在图像矢量化方面显著优于最先进的基线方法，同时支持高保真度的文本到矢量图形生成、从二维图像自动生成三维浮雕以及直观的深度感知编辑。通过将深度从物理量重新定义为创作抽象，插画师深度预测为可编辑图像分解提供了新的基础。