• 한국컴퓨터정보학회논문지
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• 제29권11호
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• pp.127-136
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• 2024

생성형 AI 기술이 발전하면서 영상 제작의 패러다임이 바뀌고 있다. 초기 낮은 품질, 일관성과 연속성의 어려움으로 인해 실제 영상의 장면으로 활용되지 못했던 단계를 지나, 최근 생성형 AI로 제작한 다양한 영상물이 영상 산업에서 활용되고 있다. 본 논문은 이러한 변화를 바탕으로 사용자 맞춤 생성형 AI의 가능성을 확인한다. 영상 산업에서 생성형 AI의 기술 발전의 방향성을 살피고, 광고, 영화, 애니메이션 분야에서의 최근 사례들을 분석하며 생성형 AI 활용도가 높아지는 원인이 높은 품질의 결과물에만 있는 것이 아니라, 콘텐츠가 가지는 본질적 목적을 수행하고 있기 때문이라는 것을 밝힌다. 이러한 과정을 통해 생성형 AI가 영상 산업에 가져올 가능성을 예측한다.

• International journal of advanced smart convergence
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• 제13권3호
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• pp.199-204
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• 2024

In the context of exploring how Artificial Intelligence(AI) can revolutionize the entertainment industry, more and more film and television productions have begun to try to intervene AI technology in various aspects of content creation. However, despite the fact that AI can generate a large amount of textual content and dynamic visual effects, it still faces challenges in terms of plot expression and delivery. This thesis explores the strengths and weaknesses, innovations, and future developments of AI technology in plot production by analyzing existing film and television productions and production practices generated using AI technology. The study proves that as AI technology continues to improve, its use in short-form production will become more and more prevalent in the future, helping human creators become more efficient and even able to produce Short Dramas in full flow.

• 전자통신동향분석
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• 제34권3호
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• pp.34-42
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• 2019

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.

• 디지털산업정보학회논문지
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• 제18권1호
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• pp.55-64
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• 2022

This study analyzes the success factors of OTT original content through big data, and intends to suggest scenarios, casting, fun, and moving elements when producing the next work. In addition, I would like to offer suggestions for the success of 'Squid Game Season 2'. The success factor of 'Squid Game' through big data is first, it is a simple psychological experimental game. Second, it is a retro strategy. Third, modern visual beauty and color. Fourth, it is simple aesthetics. Fifth, it is the platform of OTT Netflix. Sixth, Netflix's video recommendation algorithm. Seventh, it induced Binge-Watch. Lastly, it can be said that the consensus was high as it was related to the time to think about 'death' and 'money' in a pandemic situation. The suggestions for 'Squid Game Season 2' are as follows. First, it is a fusion of famous traditional games of each country. Second, it is an AI-based planned MD product production and sales strategy. Third, it is casting based on artificial intelligence big data. Fourth, secondary copyright and copyright sales strategy. The limitations of this study were analyzed only through external data. Data inside the Netflix platform was not utilized. In this study, if AI big data is used not only in the OTT field but also in entertainment and film companies, it will be possible to discover better business models and generate stable profits.