• Journal of Advanced Navigation Technology
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• v.14 no.5
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• pp.718-724
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• 2010

The Unreal Engine is a free game engine developed by Epic Games. In this paper, we propose a First Person Shooter (FPS) game development using free Unreal Development Kit. The merit of Unreal engine provide a hight degree of portability, and it is a tool used by many game developers today, supporting a multitude of platforms on personal computers and many video game consoles with free toolkit. But it is required high-performance PC to develop game. This toolkit doe s not high-quality computer programming skill, just possible to develop FPS game easily.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.489-490
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• 2024

본 논문에서는 사용자의 입장에서 게임 논리와 밸런스를 바탕으로 흥미를 유발할 수 있는 게임 개발의 요구를 고려하여 언리언 엔진 5 기반 하이퍼 FPS 게임의 설계와 구현을 제안한다. FPS 게임 내에도 정통 FPS, 배틀로얄, 그리고 하이퍼 FPS 등 여러 종류가 있는데 그 중 순전히 총으로만 싸우지 않고 캐릭터마다 고유스킬을 사용하여 전투할 수 있는 것이 하이퍼 FPS이다. 제안하는 게임 개발을 위해 다양한 게임엔진 중에서 사용자 친화적인 인터페이스와 비교적 쉬운 작업 흐름을 가지고 있는 언리얼 엔진을 선정하였다. 개발 결과의 테스트를 통해 제안한 게임과 EOS(Epic Online Services) 연동으로 다중 사용자들의 접속과 플레이를 확인하였다.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.102-118
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• 2022

With the development of digital content software production technology and the technological progress of related hardware, the social status quo in the post-epidemic era, the popularization and application of 5G networks, the market and consumers' increasing demand for digital content products, artificial intelligence, virtual digital human, virtual Idols, virtual live, self-media content and metaverse-related content industries are all developing rapidly. Virtual idols, virtual digital human, etc. are not only accelerating innovation in production technology. The economic cost, technical difficulty and time requirements of production are also greatly reduced. With the arrival and development of the Metaverse, the author believes that the content industry with virtual digital humans as the core will continue to develop in the direction of refinement, specialization, facilitation and customization. In this article, we will analyze and study the production of virtual digital human based on Photoscan technology and Unreal Engine 5 Metahuman software, and discuss the application status and future development of related content.

• Journal of Industrial Convergence
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• v.20 no.11
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• pp.19-25
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• 2022

In the development of existing games, it is judged that virtual world production was done by arranging game elements one by one. What is noteworthy here is the question of whether quality virtual worlds were efficiently produced in preparation for investment. In this study, we propose a methodology that can build an efficient virtual world based on the concept of modularization in an unreal engine. First, precedents were analyzed and five reference elements for modularization were extracted. In addition, the concept of an instance production pipeline was proposed by dividing it into four stages, and the minimum-unit instance modules for urban virtual world production were compressed into four. Finally, an urban virtual world constructed based on the minimum unit module and reference elements was implemented and presented. In conclusion, research on the production method centered on this efficiency is thought to be able to focus the time that designers or artists had to spend on production only on ideas and creativity. The limitations of the research are that the basic minimum module is limited to the city, and the derived reference elements and production pipelines have not been verified when implementing them with an unreal engine. Therefore, it is expected that various virtual world creation plans will be derived through more advanced modular research.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.755-764
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• 2019

Currently commercially available light field cameras are difficult to acquire 5D light field video since it can only acquire the still images or high price of the device. In order to solve these problems, we propose a deep learning based method for synthesizing the light field video from monocular video. To solve the problem of obtaining the light field video training data, we use UnrealCV to acquire synthetic light field data by realistic rendering of 3D graphic scene and use it for training. The proposed deep running framework synthesizes the light field video with each sub-aperture image (SAI) of $9{\times}9$ from the input monocular video. The proposed network consists of a network for predicting the appearance flow from the input image converted to the luminance image, and a network for predicting the optical flow between the adjacent light field video frames obtained from the appearance flow.

• Journal of the Korea Computer Graphics Society
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• v.28 no.5
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• pp.1-11
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• 2022

Game engines have the advantage of enabling efficient content production, such as reducing development time, with minimal visual quality guarantees and support for multi-platforms. Recently, game engines have provided various functions that can easily, quickly, and effectively produce immersive content using virtual reality (VR) HMD. Therefore, this study conducts a comparative study on the development environment in VR content production using Oculus Quest 2 HMD, focusing on Unity and Unreal game engines, which are widely used in the content production industry, including games. First, we compare the basic setup process of building a development environment using Oculus Quest 2 HMD and a dedicated controller based on a VR template project that includes the minimum functions and settings provided by each engine. Next, we present a simple experience environment that can interact in a virtual environment and compare the development environment to use a dedicated controller and the process of building a development environment that directly utilizes hands through the hand tracking function provided by Oculus Quest 2. Through this process, we will understand the basic process of building a VR development environment, and at the same time, we will check the characteristics and differences of the engine and use it as a research that can be applied to various immersive content production.

• Journal of Digital Convergence
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• v.16 no.7
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• pp.249-257
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• 2018

The purpose of this study is to construct a virtual reality simulation environment for searching and evaluating evaluation criteria for road safety facilities with new technologies. Virtual reality simulation requires high realism and accurate behavior data extraction. To do this, we used the Unreal Engine to create the environment by dividing it into an external environment and a vehicle environment. After that, a sample simulation for the luminescent road markers and preliminary experiments were conducted. As a result, luminescent road markers showed better 5m interval than 10m interval. It can be confirmed that it can be used in the simulation for searching the evaluation criteria for the new road safety facilities that incorporate the new technology in the future. In the future, it will be possible to simulate various environments by adding modeling and sample components for other road facilities.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.150-152
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• 2019

본 논문에서는 기존의 연구를 극복하여 단일 영상이 아닌 단안 비디오로부터 5D 라이트필드 영상을 합성하는 딥러닝 프레임워크를 제안한다. 현재 일반적으로 사용 가능한 Lytro Illum 카메라 등은 초당 3프레임의 비디오만을 취득할 수 있기 때문에 학습용 데이터로 사용하기에 어려움이 있다. 이러한 문제점을 해결하기 위해 본 논문에서는 가상 환경 데이터를 구성하며 이를 위해 UnrealCV를 활용하여 사실적 그래픽 렌더링에 의한 데이터를 취득하고 이를 학습에 사용한다. 제안하는 딥러닝 프레임워크는 두 개의 입력 단안 비디오에서 $5{\times}5$의 각 SAI(sub-aperture image)를 갖는 라이트필드 비디오를 합성한다. 제안하는 네트워크는 luminance 영상으로 변환된 입력 영상으로부터 appearance flow를 추측하는 플로우 추측 네트워크(flow estimation network), appearance flow로부터 얻어진 두 개의 라이트필드 비디오 프레임 간의 optical flow를 추측하는 광학 플로우 추측 네트워크(optical flow estimation network)로 구성되어있다.

• Journal of Cadastre & Land InformatiX
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• v.52 no.2
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• pp.171-187
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• 2022

Recently, the demand and development for non-face-to-face services are rapidly progressing due to the pandemic caused by the COVID-19, and attention is focused on the metaverse at the center. Entering the era of the 4th industrial revolution, Metaverse, which means a world beyond virtual and reality, combines various sensing technologies and 3D reconstruction technologies to provide various information and services to users easily and quickly. In particular, due to the miniaturization and economic increase of convergence sensors such as unmanned aerial vehicle(UAV) capable of high-resolution imaging and high-precision LiDAR(Light Detection and Ranging) sensors, research on digital-Twin is actively underway to create and simulate real-life twins. In addition, Game engines in the field of computer graphics are developing into metaverse engines by expanding strong 3D graphics reconstuction and simulation based on dynamic operations. This study constructed a mirror-world type metaverse that reflects real-world coordinate-based reality using Unreal Engine 5, a recently announced metaverse engine, with accurate 3D spatial information data of convergence sensors based on unmanned aerial system(UAS) and LiDAR. and then, spatial information contents and simulations for users were produced based on various public data to verify the accuracy of reconstruction, and through this, it was possible to confirm the construction of a more realistic and highly utilizable metaverse. In addition, when constructing a metaverse that users can intuitively and easily access through the unreal engine, various contents utilization and effectiveness could be confirmed through coordinate-based 3D spatial information with high reproducibility.

• Korean Journal of Child Studies
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• v.24 no.1
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• pp.61-76
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• 2003

This study investigated children's cognition of physically impossible televised events by age and types of tasks. The 67 subjects were 3-, 5-, and 7-year-old children from 1 day care center and 1 elementary school in Seoul. Children's responses were taped and transcribed. Cognition about the events were measured by recognition of impossibility, possibility of replication, and reasons for the possibility of replication of the events. Data were analysed by ANOVA(repeated measure) and $Scheff{\acute{e}}$ test. Children's cognitions about the events varied by age groups. Three-year-old children's recognition of the 3 tasks was different from that of the 5-and 7-year-old children; while 3-year-olds interpreted the events on TV as real, 5- and 7-year-olds considered them to be unreal.