Higher Hand-Drawn Detail Quality using Convolutional Assistant

Information

• Publication Type: Bachelor Thesis
• Workgroup(s)/Project(s):
  • Advanced Visual and Geometric Computing for 3D Capture, Display, and Fabrication
• Date: April 2020
• Date (Start): October 2019
• Date (End): April 2020
• Matrikelnummer: 01625726
• First Supervisor:
  • Joao Afonso Cardoso
  • Michael Wimmer

Abstract

The field of research in the use of neural networks to help artists or advance 2D animation is very underdeveloped. Most of the little research that is done does not even ask questions that are relevant for animators but is done in a pure research mindset. We, however, tried to find a problem that would actually be relevant in the animation industry and came up with the idea of enhancing the feature quality of poorly drawn features in 2D animation. The basis for this idea is that, as a cost and time-saving measure, in 2d animation features are often drawn in different levels of detail depending on the current focus of the scene and other factors. The focus will thereby lie on the enhancement of characters’ eyes with the idea that other features could be done in a similar way in future work. To achieve this quality enhancing we train the FUNIT network on a manually created dataset consisting of crops of eyes from different characters in different quality with the goal that it will be able to consistently transform low-quality eye images into high-quality eye images

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BibTeX

@bachelorsthesis{hanko-2019-ani,
  title =      "Higher Hand-Drawn Detail Quality using Convolutional
               Assistant",
  author =     "Dominik Hanko",
  year =       "2020",
  abstract =   "The field of research in the use of neural networks to help
               artists or advance 2D animation is very underdeveloped. Most
               of the little research that is done does not even ask
               questions that are relevant for animators but is done in a
               pure research mindset. We, however, tried to find a problem
               that would actually be relevant in the animation industry
               and came up with the idea of enhancing the feature quality
               of poorly drawn features in 2D animation. The basis for this
               idea is that, as a cost and time-saving measure, in 2d
               animation features are often drawn in different levels of
               detail depending on the current focus of the scene and other
               factors. The focus will thereby lie on the enhancement of
               characters’ eyes with the idea that other features could
               be done in a similar way in future work. To achieve this
               quality enhancing we train the FUNIT network on a manually
               created dataset consisting of crops of eyes from different
               characters in different quality with the goal that it will
               be able to consistently transform low-quality eye images
               into high-quality eye images",
  month =      apr,
  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 ",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2020/hanko-2019-ani/",
}