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"插画师深度"这一新颖的深度定义，旨在解决数字内容创作中的关键挑战：将平面图像分解为可编辑的有序图层。受艺术家构图过程的启发，插画师深度通过为每个像素推断图层索引，形成基于离散化、全局一致且针对可编辑性优化的元素排序的可解释图像分解。我们还提出并训练了一个神经网络，使用精心构建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：在图像矢量化任务中显著优于现有先进基线方法，同时实现了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概念，插画师深度预测为可编辑图像分解提供了新的理论基础。