Large Scale Materials

Guided Fine-Tuning for Large-Scale Material Transfer

Valentin Deschaintre, George Drettakis and Adrien Bousseau

Abstract

We present a method to transfer the appearance of one or a few exemplar SVBRDFs to a target image representing similar materials. Our solution is extremely simple: we fine-tune a deep appearance-capture network on the provided exemplars, such that it learns to extract similar SVBRDF values from the target image. We introduce two novel material capture and design workflows that demonstrate the strength of this simple approach. Our first workflow allows to produce plausible SVBRDFs of large-scale objects from only a few pictures. Specifically, users only need take a single picture of a large surface and a few close-up flash pictures of some of its details. We use existing methods to extract SVBRDF parameters from the close-ups, and our method to transfer these parameters to the entire surface, enabling the lightweight capture of surfaces several meters wide such as murals, floors and furniture. In our second workflow, we provide a powerful way for users to create large SVBRDFs from internet pictures by transferring the appearance of existing, pre-designed SVBRDFs. By selecting different exemplars, users can control the materials assigned to the target image, greatly enhancing the creative possibilities offered by deep appearance capture.


In this video we show some of the results obtained with our method using a couple pictures.

Paper, code, slides and supplementals

Paper’s author version
Paper’s author version (lowres)
Basilic webpage
HAL webpage

Supplemental materials for browsers
Supplemental materials

Source code
Training Data used
Weights of the pre trained network

Acknowledgements and Funding

We thank Simon Rodriguez for his help with video editing. This work was partially funded by an ANRT(http://www.anrt.asso.fr/en) CIFRE scholarship between Inria and Optis for Ansys, ERC Advanced Grant FUNGRAPH (No. 788065, http://fungraph.inria.fr), EPSRC Early Career Fellowship (EP/N006259/1) and by software donations from Adobe. The authors are grateful to Inria Sophia Antipolis – Mediterranee “Nef” computation cluster for providing resources and support (https://wiki.inria.fr/ClustersSophia/Clusters_Home)

Bibtex

@Article{DDB20,
  author       = "Deschaintre, Valentin and Drettakis, George and Bousseau, Adrien",
  title        = "Guided Fine-Tuning for Large-Scale Material Transfer",
  journal      = "Computer Graphics Forum (Proceedings of the Eurographics Symposium on Rendering)",
  number       = "4",
  volume       = "39",
  year         = "2020",
  keywords     = "material transfer, material capture, appearance capture, SVBRDF, deep learning, fine tuning",
  url          = "http://www-sop.inria.fr/reves/Basilic/2020/DDB20"
}

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