Trans- (트랜스-)

Transmedia Institute (성균관대학교 트랜스미디어연구소)
권호 표지
DOI QR코드

DOI QR Code

Case study of AI art generator using artificial intelligence

인공지능을 활용한 AI 예술 창작도구 사례 연구

  • 정지윤 (성균관대학교 인공지능혁신공유대학사업단)
  • Received : 2021.12.22
  • Accepted : 2021.01.20
  • Published : 2022.07.28
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, artificial intelligence technology is being used throughout the industry. Currently, Currently, AI art generators are used in the NFT industry, and works using them have been exhibited and sold. AI art generators in the art field include Gated Photos, Google Deep Dream, Sketch-RNN, and Auto Draw. AI art generators in the music field are Beat Blender, Google Doodle Bach, AIVA, Duet, and Neural Synth. The characteristics of AI art generators are as follows. First, AI art generator in the art field are being used to create new works based on existing work data. Second, it is possible to quickly and quickly derive creative results to provide ideas to creators, or to implement various creative materials. In the future, AI art generators are expected to have a great influence on content planning and production such as visual art, music composition, literature, and movie.

최근 인공지능 기술은 산업전반에 걸쳐서 활용되고 있다. 현재 예술 창작도구는 NFT 산업에서 사용되고 있으며, 이를 활용한 작품이 전시, 판매되기도 하였다. 미술 분야의 창작도구는 Gerated Photos, Google Deep Dream, Skech-RNN, Auto draw가 있으며, 음악분야의 인공지능 창작도구는 Beat Blender, Google Doodle Bach, AIVA, Duet, Neural Synth 등이 있다. 인공지능 예술 창작도구의 특징은 다음과 같다. 첫째, 예술분야 인공지능 창작도구는 기존의 작품 데이터를 바탕으로 새로운 작품을 창작하는 데에 활용되고 있다. 둘째, 창작 결과물을 빠르고 신속하게 도출하여 창작자에게 아이디어를 제공하거나, 창작 재료를 다양하게 구현해 볼 수 있다. 향후 인공지능 창작물은 인공지능 기술이 미술, 영상, 문학, 음악 등 콘텐츠 기획 및 제작에 많은 영향을 끼칠 것이다.

Keywords

References

  1. 강민석, 주종우, 「4차 산업혁명 시대에서 인공지능(AI) 작품 창작에 관한연구」, 「한국디지털콘텐츠학회」, 2020.
  2. Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H. 「State-of-the-art in artificial neural network applications: A survey」, 「Heliyon」, 2018.
  3. Andrew Brock, Jeff Donahue, and Karen Simonyan, 「Large scale GAN training for high fidelity natural image synthesis」, 「ICLR 2019 Conference」, 2018.
  4. Cetinic, E., She, J., 「Understanding and creating art with AI: Review and outlook. ACM Transactions on Multimedia Computing」, 「Communications and Applications」, 2022.
  5. David Ha, Douglas Eck, A neural representation of sketch drawings, ICLR 2018 Conference. 2018.
  6. Dupond, Samuel, 「A thorough review on the current advance of neural network structures」, 「Annual Reviews in Control」, 2019.
  7. Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. 「CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms」, 「ArXiv」, 2017.
  8. Hasim Sak, Andrew W. Senior, Francoise Beaufays, 「Long Short-Term Memory recurrent neural network architectures for large scale acoustic modeling」, 「Interspeech」, 2014.
  9. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, 「Generative adversarial nets」, 「Advances in neural information processing systems」, 2014.
  10. JalFaizy Shaikh, 「Build a Recurrent Neural Network from Scratch in Python - An Essential Read for Data Scientists」, 「Analytics Vidhya」, 2019.
  11. Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros, 「Unpaired image-to-image translation using cycle-consistent adversarial networks」, 「In Proceedings of the IEEE International Conference on Computer Vision(ICCV'17), 2017.
  12. Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge, 「Image style transfer using convolutional neural networks」, 「IEEE Conference on Computer Vision and Pattern Recognition」, 2016.
  13. Tero Karras, Samuli Laine, and Timo Aila, 「A style-based generator architecture for generative adversarial networks」. 「IEEE Conference on Computer Vision and Pattern Recognition (CVPR'19)」, 2019.
  14. Yongcheng Jing, Yezhou Yang, Zunlei Feng, Jingwen Ye, Yizhou Yu, and Mingli Song, 「Neural style transfer: A review」, 「IEEE Transactions on Visualization and Computer Graphics」, 2019.
  15. AVIA https://www.aiva.ai/
  16. DeepDream Generator https://deepdreamgenerator.com/
  17. DeepDream Tensorflow Source https://www.tensorflow.org/tutorials/generative/deepdream
  18. Infinite Herbarium https://infiniteherbarium.withgoogle.com/
  19. Generated Photos https://generated.photos/
  20. Google Doodle https://www.google.com/doodles/celebrating-johann-sebastian-bach
  21. https://www.analyticsvidhya.com/blog/2019/01/fundamentals-deep-learning-recurrent-neural-networks-scratch-python/
  22. Magenta Project https://magenta.tensorflow.org/
  23. MuseGAN Model https://salu133445.github.io/musegan/model
  24. The Next Rambrant Website https://www.nextrembrandt.com