We study inferring a tree-structured representation from a single image for object shading. Prior work typically uses the parametric or measured representation to model shading, which is neither interpretable nor easily editable. We propose using the shade tree representation, which combines basic shading nodes and compositing methods to factorize object surface shading. The shade tree representation enables novice users who are unfamiliar with the physical shading process to edit object shading in an efficient and intuitive manner. A main challenge in inferring the shade tree is that the inference problem involves both the discrete tree structure and the continuous parameters of the tree nodes. We propose a hybrid approach to address this issue. We introduce an auto-regressive inference model to generate a rough estimation of the tree structure and node parameters, and then we fine-tune the inferred shade tree through an optimization algorithm. We show experiments on synthetic images, captured reflectance, real images, and non-realistic vector drawings, allowing downstream applications such as material editing, vectorized shading, and relighting. Project website: https://chen-geng.com/inv-shade-trees

翻译：我们研究从单张图像中推断出物体阴影的树状结构表示。先前的工作通常使用参数化或测量化表示来建模阴影，这种表示既不可解释也不易编辑。我们提出使用阴影树表示，该表示结合了基础阴影节点与合成方法，以实现物体表面阴影的分解。阴影树表示使不熟悉物理阴影过程的新手用户能够高效且直观地编辑物体阴影。推断阴影树的主要挑战在于：推理问题既涉及离散的树结构，又涉及树节点的连续参数。我们提出了一种混合方法来解决这一问题。我们引入自回归推理模型以生成树结构与节点参数的粗略估计，随后通过优化算法对推断出的阴影树进行微调。我们在合成图像、捕获的反射率、真实图像以及非真实感矢量图上进行了实验，支持诸如材质编辑、矢量阴影及重光照等下游应用。项目网站：https://chen-geng.com/inv-shade-trees