• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3284-3306
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• 2018

This study proposes a method to develop an authoring system(GeoMaTree) for diverse trees that constitute a virtual landscape. The GeoMaTree system enables the simple, intuitive production of an efficient structure, and supports real-time processing. The core of the proposed system is a procedural modeling based on a mathematical model and an application that supports digital content creation on diverse platforms. The procedural modeling allows users to control the complex pattern of branch propagation through an intuitive process. The application is a multi-resolution 3D model that supports appropriate optimization for a tree structure. The application and a compatible function, with commercial tools for supporting the creation of realistic synthetic images and virtual landscapes, are implemented, and the proposed system is applied to a variety of 3D image content.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2232-2245
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• 2013

The present study proposes efficient procedural modeling methods for enabling the growth and creation of various trees with minimal user control. Growth volume algorithms are utilized in order to easily and effectively calculate many parameters that determine tree growth, including branch propagation. Procedural methods are designed so that users' interactive control structures can be applied to these algorithms to create unique tree models efficiently. First, through a two-line-based interactive growth volume control method, the growth information that determines the overall shape of the tree is intuitively adjusted. Thereafter, independent branch control methods designed to control individual branches are added to the growth deformation in order to enable the growth of unique trees. Whether the growth processes of desired trees can be easily and intuitively controlled by the proposed method is verified through experiments. Methods that can apply the proposed methods are also verified.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.784-789
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• 2009

We propose a novel method of procedurally generating climbing plants using L-systems. The goal of this research is to generate geometry for 3D-modelers, where procedurally generated content is used as a base for the final design. The algorithm is fast and efficiently simulates external tropisms such as gravitropism and heliotropism, as well pseudo-tropisms. The structure of the generated climbing plants is discretized into strings of particles expressed using L-systems. The tips of the plant extend the branches by adding particles in its path, forming internodes. A climbing heuristic has been developed that uses the environment as leverage when the plant is climbing, and effectively covers objects on which it grows. A fast method that sprouts leaves on the surface on which the plant is growing has also been developed, along with a heuristic that simulates the decrease in length, radius and leaf size.

• Journal of Korea Game Society
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• v.18 no.2
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• pp.89-98
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• 2018

The recent Neural Network technique has shown good performance in content generation such as image generation in addition to the conventional classification problem and clustering problem solving. In this study, we propose an image generation method using artificial neural network as a next generation content creation technique. The proposed artificial neural network model receives two images and combines them into a new image by taking color from one image and shape from the other image. This model is made up of Convolutional Neural Network, which has two encoders for extracting color and shape from images, and a decoder for taking all the values of each encoder and generating a combination image. The result of this work can be applied to various 2D image generation and modification works in game development process at low cost.