• International Journal of Advanced Culture Technology
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• 제11권3호
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• pp.225-230
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• 2023

With the development of AI technology, there is a growing discussion about Text-to-Image Generative AI. We presented a Generative AI video production method and delineated a methodology for the production of personalized AI-generated videos with the objective of broadening the landscape of the video domain. And we meticulously examined the procedural steps involved in AI-driven video production and directly implemented a video creation approach utilizing the VQGAN-CLIP model. The outcomes produced by the VQGAN-CLIP model exhibited a relatively moderate resolution and frame rate, and predominantly manifested as abstract images. Such characteristics indicated potential applicability in OTT-based video content or the realm of visual arts. It is anticipated that AI-driven video production techniques will see heightened utilization in forthcoming endeavors.

• 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.

• International Journal of Internet, Broadcasting and Communication
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• 제16권1호
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• pp.86-91
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• 2024

In the field of artistic creation, the iterative development of AI-generated video software has pushed the boundaries of multimedia content creation and provided powerful creative tools for non-professionals. This paper extensively examines two leading AI-generated video software, PIKA Labs and RUNWAY, discussing their functions, performance differences, and application scopes in the video generation domain. Through detailed operational examples, a comparative analysis of their functionalities, as well as the advantages and limitations of each in generating video content, is presented. By comparison, it can be found that PIKA Labs and RUNWAY have excellent performance in stability and creativity. Therefore, the purpose of this study is to comprehensively elucidate the operating mechanisms of these two AI software, in order to intuitively demonstrate the advantages of each software. Simultaneously, this study provides valuable references for professionals and creators in the video production field, assisting them in selecting the most suitable tools for different scenarios, thereby advancing the application and development of AI-generated video software in multimedia content creation.

• International Journal of Advanced Culture Technology
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• 제11권3호
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• pp.253-259
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• 2023

The increasing incorporation of AI in video storyboard creation has been observed recently. Traditionally, the production of storyboards requires significant time, cost, and specialized expertise. However, the integration of AI can amplify the efficiency of storyboard creation and enhance storytelling. In Korea, AiSAC stands at the forefront of AI-driven storyboard platforms, boasting the capability to generate realistic images built on open datasets foundations. Yet, a notable limitation is the difficulty in intricately conveying a director's vision within the storyboard. To address this challenge, we proposed the application of image generation features from ChatGPT and Midjourney to AiSAC. Through this research, we aimed to enhance the efficiency of storyboard production and refined the intricacy of expression, thereby facilitating advancements in the video production process.

• Journal of Information Processing Systems
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• 제19권4호
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• pp.439-449
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• 2023

This paper deals with a resource efficient artificial intelligence (AI) service architecture for multi-channel video streams. As an AI service, we consider the object detection model, which is the most representative for video applications. Since most object detection models are basically designed for a single channel video stream, the utilization of the additional resource for multi-channel video stream processing is inevitable. Therefore, we propose a resource efficient AI service framework, which can be associated with various AI service models. Our framework is designed based on the modular architecture, which consists of adaptive frame control (AFC) Manager, multiplexer (MUX), adaptive channel selector (ACS), and YOLO interface units. In order to run only a single YOLO process without regard to the number of channels, we propose a novel approach efficiently dealing with multi-channel input streams. Through the experiment, it is shown that the framework is capable of performing object detection service with minimum resource utilization even in the circumstance of multi-channel streams. In addition, each service can be guaranteed within a deadline.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.312-318
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• 2022

From the past two eras, artificial intelligence has gained the attention of researchers of all research areas. Video editing is a task in the list that starts leveraging the blessing of Artificial Intelligence (AI). Since AI promises to make technology better use of human life although video editing technology is not new yet it is adopting new technologies like AI to become more powerful and sophisticated for video editors as well as users. Like other technologies, video editing will also be facilitated by the majestic power of AI in near future. There has been a lot of research that uses AI in video editing, yet there is no comprehensive literature review that systematically finds all of this work on one page so that new researchers can find research gaps in that area. In this research we conducted a statically approach called, systematic mapping study, to find answers to pre-proposed research questions. The aim and objective of this research are to find research gaps in our topic under discussion.

• International Journal of Internet, Broadcasting and Communication
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• 제14권2호
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• pp.212-218
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• 2022

In this paper, the necessity of AI video interview arises when conducting an interview for acquisition of excellent talent in a non-face-to-face situation due to similar situations such as Covid-19. As a matter to be supplemented in general AI interviews, it is difficult to evaluate the reliability and qualitative factors. In addition, the AI interview is conducted not in a two-way Q&A, rather in a one-sided Q&A process. This paper intends to fuse the advantages of existing AI interviews and video interviews. When conducting an interview using AI image analysis technology, it supplements subjective information that evaluates interview management and provides quantitative analysis data and HR expert data. In this paper, image-based multi-modal AI image analysis technology, bioanalysis-based HR analysis technology, and web RTC-based P2P image communication technology are applied. The goal of applying this technology is to propose a method in which biological analysis results (gaze, posture, voice, gesture, landmark) and HR information (opinions or features based on user propensity) can be processed on a single screen to select the right person for the hire.

• 지능정보연구
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• 제29권4호
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• pp.15-30
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• 2023

최근 미디어 분야에도 인공지능(AI)을 적용한 다양한 서비스가 등장하고 있는 추세이다. 하지만 편집점을 찾아 영상을 이어 붙이는 영상 편집은, 대부분 수동적 방식으로 진행되어 시간과 인적 자원의 소요가 많이 발생하고 있다. 이에 본 연구에서는 Video Swin Transformer를 활용하여, 발화 여부에 따른 영상의 편집점을 탐지할 수 있는 방법론을 제안한다. 이를 위해, 제안 구조는 먼저 Face Alignment를 통해 얼굴 특징점을 검출한다. 이와 같은 과정을 통해 입력 영상 데이터로부터 발화 여부에 따른 얼굴의 시 공간적인 변화를 모델에 반영한다. 그리고, 본 연구에서 제안하는 Video Swin Transformer 기반 모델을 통해 영상 속 사람의 행동을 분류한다. 구체적으로 비디오 데이터로부터 Video Swin Transformer를 통해 생성되는 Feature Map과 Face Alignment를 통해 검출된 얼굴 특징점을 합친 후 Convolution을 거쳐 발화 여부를 탐지하게 된다. 실험 결과, 본 논문에서 제안한 얼굴 특징점을 활용한 영상 편집점 탐지 모델을 사용했을 경우 분류 성능을 89.17% 기록하여, 얼굴 특징점을 사용하지 않았을 때의 성능 87.46% 대비 성능을 향상시키는 것을 확인할 수 있었다.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제21권8호
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• pp.572-577
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• 2015

보통의 인공지능 시스템은 특정 작업을 수행하기 위해 설계되며, 해당 작업만을 수행하는 능력을 가진다. 그에 반해 인공 일반지능이란 설계 당시 목표로 한 작업만이 아니라 새로 접하는 다양한 문제에도 대응할 수 있는 인공지능을 의미한다. 최근 게임 인공지능 분야의 일반지능 문제인 General Video Game Playing에 대한 관심이 높아지고 있다. 비디오 게임으로 범위가 제한되었지만, 다양한 형태의 비디오 게임을 플레이 할 수 있는 단일 인공지능을 설계하는 것은 상당히 도전적인 문제이다. 본 논문에서는 Monte-Carlo Tree Search를 이용하는 기존 비디오 게임을 위한 인공 일반지능을 개선하는 방법에 대해 기술한다. 여기서는 UCB1 알고리즘을 문제에 적합하도록 개선한 GreedyUCB1과 게임 분석을 통해 얻은 지식을 활용한 Rollout 방법을 제안한다. 제안한 방법으로 개발된 인공지능은 국제 학술대회인 IEEE Computational Intelligence in Games의 2014년 인공지능 경진 대회에 출전하여 4위의 성적을 보였다.

• 문화기술의 융합
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• 제7권4호
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• pp.415-420
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• 2021

의료 케어 분야가 다각도로 발전되어가고 있는 상황에서 의료 홍보 영상분석은 중요한 의미를 지닌다. 경쟁력 향상의 문제로 중요성을 있으며 AI 시스템의 가속화 시대는 의료 케어가 가장 선두에 있는 분야이기도 하다. 이에 따른 홍보와 광고 및 설명에 대한 영상의 중요성은 매우 중요하며 기업의 이미지를 전환 시킬 수 있는 중요한 방향이기도 하다. 본 연구에서는 AI 의료 브랜드들의 전문 영상에 대한 비교분석을 중심으로 해외 메이져 회사 2개, 스트라이커와 힐롬 (Stryker, Hill-rom)의 회사와 국내 선두기업 1개(나인벨)의 영상을 중심으로 영상에서의 디자인 특징과 차이점 등을 비교하였고, 그에 따른 세부 파트 분석과 섹션 분석을 하였다. 영상 편집의 기술적 부분 분석으로, 트렌제이션 방식 및 인포 그래픽들을 고찰하였다. 심층 비교는 AI 의료 영상의 영상 색상 톤과 영상 배색 관계에 대한 차이점과 같은점들을 비교 분석하였다. 영상 이미지 결정 부분에 있어서 구체적인 분석은 각 영상들의 영상 인트로 부분과 제품 설명 영상 부분들의 구체적 장면을 가지고, 홍보 디자인에서 나타나고 있는 차별화된 요소들을 비교 연구하였다.