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Exploratory Experimental Analysis for 2D to 3D Generation

2D to 3D 창의적 생성을 위한 탐색적 실험 분석

  • Hyeongrae Cho (Department of Electronic IT Media Engineering, Seoul National University of Science and Technology) ;
  • Ilsik Chang (Department of Electronic IT Media Engineering, Seoul National University of Science and Technology) ;
  • Hyunseok Kang (Department of Electronic IT Media Engineering, Seoul National University of Science and Technology) ;
  • Youngchan Go (Department of Electronic IT Media Engineering, Seoul National University of Science and Technology) ;
  • Gooman Park (Department of Electronic IT Media Engineering, Seoul National University of Science and Technology)
  • 조형래 (서울과학기술대학교 전자IT미디어공학과) ;
  • 장일식 (서울과학기술대학교 전자IT미디어공학과) ;
  • 강현석 (서울과학기술대학교 전자IT미디어공학과) ;
  • 고영찬 (서울과학기술대학교 전자IT미디어공학과) ;
  • 박구만 (서울과학기술대학교 전자IT미디어공학과)
  • Received : 2022.11.29
  • Accepted : 2023.01.24
  • Published : 2023.01.30

Abstract

Deep learning has made rapid progress in recent years and is affecting various fields and industries. The art field cannot be an exception, and in this paper, we would like to explore and experiment and analyze research fields that creatively generate 2D images in 3D from a visual arts and engineering perspective. To this end, the original image of the domestic artist is learned through GAN or Diffusion Models, and then converted into 3D using 3D conversion software and deep learning. And we compare the results with prior algorithms. After that, we will analyze the problems and improvements of 2D to 3D creative generation.

딥러닝은 최근 몇 년 동안 비약적인 발전을 하였고 다양한 분야 및 산업에 영향을 주고 있다. 예술영역도 예외일 수는 없는데 본 논문에서는 시각예술·공학적 관점에서 2D 이미지를 3D로 창의적으로 생성하는 방법을 실험하고자 한다. 이를 위해 국내 아티스트 원본 이미지를 GAN 또는 Diffusion Models로 학습시킨 후 3D 변환 소프트웨어와 딥러닝을 활용하여 3D로 변환하고 그 결과를 선행연구 알고리즘과 비교 실험함으로써 2D to 3D 창의적 생성의 문제점과 개선점을 분석하고자 한다.

Keywords

Acknowledgement

이 글은 2022년도 과학기술정보통신부의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임 (No.2021-0-00751, 0.5mm급 이하 초정밀 가시·비가시 정보 표출을 위한 다차원 시각화 디지털 트윈 프레임워크 기술개발).

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