Match: differentiable material graphs for procedural material capture
Name
3414685.3417781.pdf
Description
Published version
Size
24.41 MB
Format
Adobe PDF
Checksum (MD5)
8cc51f1ef0b87757f4435e836c15ad26
Author(s)
Shi, Liang
Li, Beichen
Hašan, Miloš
Sunkavalli, Kalyan
Boubekeur, Tamy
Mech, Radomir
Matusik, Wojciech
Date Issued
2020
Journal
ACM Transactions on Graphics
Publisher
Association for Computing Machinery (ACM)
Version
Final published version
Abstract
© 2020 Owner/Author. We present MATch, a method to automatically convert photographs of material samples into production-grade procedural material models. At the core of MATch is a new library DiffMat that provides differentiable building blocks for constructing procedural materials, and automatic translation of large-scale procedural models, with hundreds to thousands of node parameters, into differentiable node graphs. Combining these translated node graphs with a rendering layer yields an end-to-end differentiable pipeline that maps node graph parameters to rendered images. This facilitates the use of gradient-based optimization to estimate the parameters such that the resulting material, when rendered, matches the target image appearance, as quantified by a style transfer loss. In addition, we propose a deep neural feature-based graph selection and parameter initialization method that efficiently scales to a large number of procedural graphs. We evaluate our method on both rendered synthetic materials and real materials captured as flash photographs. We demonstrate that MATch can reconstruct more accurate, general, and complex procedural materials compared to the state-of-the-art. Moreover, by producing a procedural output, we unlock capabilities such as constructing arbitrary-resolution material maps and parametrically editing the material appearance.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3414685.3417781
