• Journal of Information Technology Applications and Management
• /
• v.31 no.4
• /
• pp.99-119
• /
• 2024

Focusing on the rapid progress of image generation AI, this study examines the changes in talent required according to changes in the production process of the content industry, and proposes an educational management plan for the subject and comparative department of the university's animation major. First, through environmental analysis, the trend of the animation content industry is analyzed in three stages, and the necessity of producing AI-adapted content talent is derived by re-establishing the talent image of the university's animation major and introducing it into rapid education. Next, we present a case designed by applying teaching methods to improve technology convergence capabilities and project-oriented capabilities by presenting subject and non-curricular cases operated in the animation department of the researcher's university. Through this, we propose the necessity of education to cultivate animation content talent who can play technical and administrative roles by utilizing various AI systems in the future. The goal of this study is to establish a cornerstone study by presenting application cases and having the status of a university as a talent supplier that can lead the content industry beyond the era of AI content production that breaks the boundaries of genres between contents. In conclusion, it is intended to propose the application of education to create value through technology convergence capabilities and project-oriented capabilities to cultivate AI-adapted content talents.

• Journal of Information Technology Applications and Management
• /
• v.30 no.5
• /
• pp.107-120
• /
• 2023

In this study, we investigate the changes and realities of the content production process focusing on Image generation AI revolutions such as Stable Diffusion, Midjourney, and DELL-E, and examine the current status of related department operations at universities and Find out the status of the current curriculum. Through this, we suggest the need to produce AI-adaptive content talent through re-establishing the capabilities of content-related departments in art universities and quickly introducing curriculum. This is because it can be input into the efficient AI content development system currently being applied in industrial fields, and it is necessary to cultivate talent who can perform managerial and technical roles using various AI systems in the future. In conclusion, we will prepare cornerstone research to establish the university's status as a source of talent that can lead the content industry beyond the AI content production era, and focus on convergence capabilities and experience with the goal of producing convergence talent to cultivate AI adaptive content talent, suggests the direction of curriculum application for value creation.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2024.01a
• /
• pp.481-484
• /
• 2024

본 논문에서는 레고(LEGO®) 조립 설명서를 생성하기 위해 딥러닝을 이용한 조립 및 설명서 생성 시스템을 제안한다. 이 시스템은 사용자가 제공한 단일 이미지를 기반으로 레고 조립 설명서를 자동 생성한다. 해당 시스템은 딥러닝 기반 이미지 분할 기술을 활용하여 물체를 배경으로부터 분리하고 이를 통해 조립 설명서를 생성하는 과정을 포함하며, 조립을 위한 알고리즘을 새로 설계하였다. 이 시스템은 기존 레고 제품의 한계를 극복하고, 사용자에게 주어진 부품으로 다양한 모델을 자유롭게 조립할 수 있게 한다. 또한, 복잡한 레고 조립 과정을 간소화하고, 조립의 장벽을 낮추는 데 도움을 준다.

• Investigative Magnetic Resonance Imaging
• /
• v.26 no.1
• /
• pp.1-9
• /
• 2022

Artificial intelligence (AI) powered by deep learning (DL) has shown remarkable progress in image recognition tasks. Over the past decade, AI has proven its feasibility for applications in medical imaging. Various aspects of clinical practice in neuroimaging can be improved with the help of AI. For example, AI can aid in detecting brain metastases, predicting treatment response of brain tumors, generating a parametric map of dynamic contrast-enhanced MRI, and enhancing radiomics research by extracting salient features from input images. In addition, image quality can be improved via AI-based image reconstruction or motion artifact reduction. In this review, we summarize recent clinical applications of DL in various aspects of neuroimaging.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.4
• /
• pp.667-679
• /
• 2023

The advent of image generative AI has made it possible for people who are not experts in art and design to create finished artworks through text input. With the increasing availability of generated images and their impact on the art industry, there is a need for research on how users perceive the process of co-creating with AI. In this study, we conducted an experimental study to investigate the expected and experienced processes of image generative AI creation among general users and to find out which processes affect users' sense of creative agency. The results showed that there was a gap between the expected and experienced creative process, and users tended to perceive a low sense of creative agency. We recommend eight ways that AI can act as an enabler to support users' creative intentions so that they can experience a higher sense of creative agency. This study can contribute to the future development of image-generating AI by considering user-centered creative experiences.

• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.4
• /
• pp.166-171
• /
• 2023

With significant advancements in the field of artificial intelligence, many novel algorithms and technologies have emerged. Currently, AI painting can generate high-quality images based on textual descriptions. However, it is often challenging to control details when generating images, even with complex textual inputs. Therefore, there is a need to implement additional control mechanisms beyond textual descriptions. Based on ControlNet, this passage describes a combined utilization of various local controls (such as edge maps and depth maps) and global control within a single model. It provides a comprehensive exposition of the fundamental concepts of ControlNet, elucidating its theoretical foundation and relevant technological features. Furthermore, combining methods and applications, understanding the technical characteristics involves analyzing distinct advantages and image differences. This further explores insights into the development of image generation patterns.

• International Journal of Internet, Broadcasting and Communication
• /
• v.16 no.1
• /
• pp.86-91
• /
• 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.

• Journal of KIBIM
• /
• v.14 no.2
• /
• pp.13-24
• /
• 2024

This research suggests a novel visualization approach utilizing Generative AI to render photorealistic architectural alternatives images in the early design phase. Photorealistic rendering intuitively describes alternatives and facilitates clear communication between stakeholders. Nevertheless, the conventional rendering process, utilizing 3D modelling and rendering engines, demands sophisticate model and processing time. In this context, the paper suggests a rendering approach employing the text-to-image method aimed at generating a broader range of intuitive and relevant reference images. Additionally, it employs an Text-to-Image method focused on producing a diverse array of alternatives reflecting architects' styles when visualizing the exteriors of residential buildings from the mass model images. To achieve this, fine-tuning for architects' styles was conducted using the Low-Rank Adaptation (LoRA) method. This approach, supported by fine-tuned models, allows not only single style-applied alternatives, but also the fusion of two or more styles to generate new alternatives. Using the proposed approach, we generated more than 15,000 meaningful images, with each image taking only about 5 seconds to produce. This demonstrates that the Generative AI-based visualization approach significantly reduces the labour and time required in conventional visualization processes, holding significant potential for transforming abstract ideas into tangible images, even in the early stages of design.

• Electronics and Telecommunications Trends
• /
• v.38 no.5
• /
• pp.1-11
• /
• 2023

The emergence of generative hyperscale artificial intelligence (AI) has enabled new services, such as image-generating AI and conversational AI based on large language models. Such services likely lead to the influx of numerous users, who cannot be handled using conventional AI models. Furthermore, the exponential increase in training data, computations, and high user demand of AI models has led to intensive hardware resource consumption, highlighting the need to develop domain-specific semiconductors for hyperscale AI. In this technical report, we describe development trends in technologies for hyperscale AI processors pursued by domestic and foreign semiconductor companies, such as NVIDIA, Graphcore, Tesla, Google, Meta, SAPEON, FuriosaAI, and Rebellions.

• International Journal of Internet, Broadcasting and Communication
• /
• v.16 no.1
• /
• pp.99-105
• /
• 2024

Runway released an updated version, Gen-2, in March 2023, which introduced new features that are different from Gen-1: it can convert text and images into videos, or convert text and images together into video images based on text instructions. This update will be officially open to the public in June 2023, so more people can enjoy and use their creativity. With this new feature, users can easily transform text and images into impressive video creations. However, as with all new technologies, comes the instability of AI, which also affects the results generated by Runway. This article verifies the feasibility of using Runway to generate the desired video from several aspects through personal practice. In practice, I discovered Runway generation problems and propose improvement methods to find ways to improve the accuracy of Runway generation. And found that although the instability of AI is a factor that needs attention, through careful adjustment and testing, users can still make full use of this feature and create stunning video works. This update marks the beginning of a more innovative and diverse future for the digital creative field.