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Hair and Fur Synthesizer via ConvNet Using Strand Geometry Images

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(5), pp.85-92
  • DOI : 10.9708/jksci.2022.27.05.085
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : March 31, 2022
  • Accepted : April 28, 2022
  • Published : May 31, 2022

Jong-Hyun Kim 1

1강남대학교

Accredited

ABSTRACT

In this paper, we propose a technique that can express low-resolution hair and fur simulations in high-resolution without noise using ConvNet and geometric images of strands in the form of lines. Pairs between low-resolution and high-resolution data can be obtained through physics-based simulation, and a low-resolution-high-resolution data pair is established using the obtained data. The data used for training is used by converting the position of the hair strands into a geometric image. The hair and fur network proposed in this paper is used for an image synthesizer that upscales a low-resolution image to a high-resolution image. If the high-resolution geometry image obtained as a result of the test is converted back to high-resolution hair, it is possible to express the elastic movement of hair, which is difficult to express with a single mapping function. As for the performance of the synthesis result, it showed faster performance than the traditional physics-based simulation, and it can be easily executed without knowing complex numerical analysis.

Citation status

* References for papers published after 2023 are currently being built.