Tree-Structured Shading Decomposition
Chen Geng, Hong-Xing Yu, Sharon Zhang, Maneesh Agrawala, Jiajun Wu

TL;DR
This paper introduces a novel shade tree representation for object shading inference from a single image, enabling intuitive editing and applications like relighting, by combining a hybrid inference approach for structure and parameters.
Contribution
It proposes a new shade tree model for shading decomposition that is interpretable and editable, along with a hybrid inference method for structure and parameter estimation.
Findings
Effective on synthetic and real images
Enables intuitive shading editing
Supports downstream applications like relighting
Abstract
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…
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Taxonomy
TopicsComputer Graphics and Visualization Techniques · 3D Shape Modeling and Analysis · Advanced Vision and Imaging
