Photorealistic Material Learning and Synthesis

Information

• Publication Type: PhD-Thesis
• Workgroup(s)/Project(s):
  • Path-Space Manifolds for Noise-Free Light Transport
• Date: December 2019
• Date (Start): 2013
• Date (End): 2019
• Open Access: yes
• 1st Reviewer: Torsten Möller
• 2nd Reviewer: Derek Nowrouzezahrai
• Rigorosum: January 2020
• First Supervisor: Michael Wimmer
• Keywords: neural rendering, machine learning, photorealistic rendering, ray tracing, global illumination, material synthesis

Abstract

Light transport simulations are the industry-standard way of creating convincing photorealistic imagery and are widely used in creating animation movies, computer animations, medical and architectural visualizations among many other notable applications. These techniques simulate how millions of rays of light interact with a virtual scene, where the realism of the final output depends greatly on the quality of the used materials and the geometry of the objects within this scene. In this thesis, we endeavor to address two key issues pertaining to photorealistic material synthesis: first, creating convincing photorealistic materials requires years of expertise in this field and requires a non-trivial amount of trial and error from the side of the artist. We propose two learning-based methods that enables novice users to easily and quickly synthesize photorealistic materials by learning their preferences and recommending arbitrarily many new material models that are in line with their artistic vision. We also augmented these systems with a neural renderer that performs accurate light-transport simulation for these materials orders of magnitude quicker than the photorealistic rendering engines commonly used for these tasks. As a result, novice users are now able to perform mass-scale material synthesis, and even expert users experience a significant improvement in modeling times when many material models are sought.

Second, simulating subsurface light transport leads to convincing translucent material visualizations, however, most published techniques either take several hours to compute an image, or make simplifying assumptions regarding the underlying physical laws of volumetric scattering. We propose a set of real-time methods to remedy this issue by decomposing well-known 2D convolution filters into a set of separable 1D convolutions while retaining a high degree of visual accuracy. These methods execute within a few milliseconds and can be inserted into state-of-the-art rendering systems as a simple post-processing step without introducing intrusive changes into the rendering pipeline.

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BibTeX

@phdthesis{zsolnai-feher-thesis-2019,
  title =      "Photorealistic Material Learning and Synthesis",
  author =     "Karoly Zsolnai-Feh\'{e}r",
  year =       "2019",
  abstract =   "Light transport simulations are the industry-standard way of
               creating convincing photorealistic imagery and are widely
               used in creating animation movies, computer animations,
               medical and architectural visualizations among many other
               notable applications. These techniques simulate how millions
               of rays of light interact with a virtual scene, where the
               realism of the final output depends greatly on the quality
               of the used materials and the geometry of the objects within
               this scene. In this thesis, we endeavor to address two key
               issues pertaining to photorealistic material synthesis:
               first, creating convincing photorealistic materials requires
               years of expertise in this field and requires a non-trivial
               amount of trial and error from the side of the artist. We
               propose two learning-based methods that enables novice users
               to easily and quickly synthesize photorealistic materials by
               learning their preferences and recommending arbitrarily many
               new material models that are in line with their artistic
               vision. We also augmented these systems with a neural
               renderer that performs accurate light-transport simulation
               for these materials orders of magnitude quicker than the
               photorealistic rendering engines commonly used for these
               tasks. As a result, novice users are now able to perform
               mass-scale material synthesis, and even expert users
               experience a significant improvement in modeling times when
               many material models are sought.  Second, simulating
               subsurface light transport leads to convincing translucent
               material visualizations, however, most published techniques
               either take several hours to compute an image, or make
               simplifying assumptions regarding the underlying physical
               laws of volumetric scattering. We propose a set of real-time
               methods to remedy this issue by decomposing well-known 2D
               convolution filters into a set of separable 1D convolutions
               while retaining a high degree of visual accuracy. These
               methods execute within a few milliseconds and can be
               inserted into state-of-the-art rendering systems as a simple
               post-processing step without introducing intrusive changes
               into the rendering pipeline.",
  month =      dec,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien ",
  keywords =   "neural rendering, machine learning, photorealistic
               rendering, ray tracing, global illumination, material
               synthesis",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2019/zsolnai-feher-thesis-2019/",
}