2D to 3D 창의적 생성을 위한 탐색적 실험 분석

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.