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Artificial Intelligence-Based Video Content Generation

인공지능 기반 영상 콘텐츠 생성 기술 동향

  • Published : 2019.06.01

Abstract

This study introduces artificial intelligence (AI) techniques for video generation. For an effective illustration, techniques for video generation are classified as either semi-automatic or automatic. First, we discuss some recent achievements in semi-automatic video generation, and explain which types of AI techniques can be applied to produce films and improve film quality. Additionally, we provide an example of video content that has been generated by using AI techniques. Then, two automatic video-generation techniques are introduced with technical details. As there is currently no feasible automatic video-generation technique that can generate commercial videos, in this study, we explain their technical details, and suggest the future direction for researchers. Finally, we discuss several considerations for more practical automatic video-generation techniques.

Keywords

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그림 1 창작 플랫폼의 장면 검색 화면

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그림 2 창작 플랫폼의 스토리 구성 화면

표 1 영상 콘텐츠 생성 기술 분류별 장단점

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References

  1. I. J. Goodfellow et al., "Generative Adversarial Nets," in Adv. Neural Inform. Process. Syst., Montreal, Canada, 2014, pp. 2672-2680.
  2. A. Odena, C. Olah, J. Shlens, "Conditional Image Synthesis with Auxiliary Classifier GANs," in Proc. Int. Conf. Mach. Learning, Sydney Australia, 2017, pp. 2642-2651.
  3. Z. Yi, H. Zhang, P. Tan, M. Gong, "DualGAN: Unsupervised Dual Learning for Image-to-Image Translation," in Proc. IEEE Int. Conf. Comput. Vision, Venice, Italy, Oct. 2017, pp. 2868-2876.
  4. rossgoodwin, "WordCar," GitHub, available: https://github.com/rossgoodwin/wordcar
  5. B. Liu, J. Fu, M. Kato, M. Yoshikawa, "Beyond Narrative Description: Generating Poetry from Images by Multi-Adversarial Training," in Proc. ACM Int. Conf. Multimedia, Seoul, Rep. of Korea, Oct. 2018, pp. 783-791.
  6. A. Mordvintsev, C. Olah, M. Tyka, "DeepDream - a Code Example for Visualizing Neural Networks," Google Research, 2015.
  7. R. Goodwin, O. Sharp, "Benjamin," http://benjamin-ai.tumblr.com.
  8. A. Lee, C. Kwak, J. Son, S. Kim, "SVIAS: Scene-segmented Video Information Annotation System," in Proc. ACM Int. Conf. Multimedia, Seoul, Rep. of Korea, Oct. 2018, pp. 1278-1269.
  9. C. Kwak, M. Han, S. Kim, G. Hahm, "Interactive Story Maker: Tagged Video Retrieval System for Video Re-creation Service," in Proc. ACM Int. Conf. Multimedia, Seoul, Rep. of Korea, Oct. 2018, pp. 1270-1271.
  10. R. Villegas, J. Yang, S. Hong, X. Lin, H. Lee, "Decomposing Motion and Content for Natural Video Sequence Prediction," in Proc. Int. Conf. Learning Representations, Toulon, France, Apr. 24-26, 2017.
  11. S. Tulyakov, M. Liu, X. Yang, J. Kautz, "MoCoGAN: Decomposing Motion and Content for Video Generation," in Proc. IEEE Conf. Comput. Vision Pattern Recogn., Salt Lake City, UT, USA, June 18-23, pp. 1526-1535.
  12. IBM THINK Blog, "IBM Research Takes Watson to Holloywood with the First Cognitive Movie Trailer," Aug. 31, 2016.
  13. J. Brandon, "Terrifying High-Tech Porn: Creepy 'Deepfake' Videos are on the Rise," Fox News, Feb. 20, 2018.
  14. S. Machkovech, "This Wild, AI-Generated Film is the Next Step in 'Whole-Movie Puppetry'," Arstechnica, June 12, 2018.
  15. 조원진, "부산시와 ETRI, 인터랙티브 영상 공모전 개최," 서울경제, June 11, 2018.
  16. A. Conner-Simons, R. Gordon, "Creating Videos of the Future," MIT News, Nov. 28, 2018.
  17. B. Lotter, G. Kreiman, D. Cox, "Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning," arXiv:1605.08104, 2016.