• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1794-1806
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• 2023

This study presents a method for capturing photographs of users as input and converting them into 2D character animation sprites using a generative adversarial network-based artificial intelligence network. Traditionally, 2D character animations have been created by manually creating an entire sequence of sprite images, which incurs high development costs. To address this issue, this study proposes a technique that combines motion videos and sample 2D images. In the 2D sprite generation process that uses the proposed technique, a sequence of images is extracted from real-life images captured by the user, and these are combined with character images from within the game. Our research aims to leverage cutting-edge deep learning-based image manipulation techniques, such as the GAN-based motion transfer network (impersonator) and background noise removal (U² -Net), to generate a sequence of animation sprites from a single image. The proposed technique enables the creation of diverse animations and motions just one image. By utilizing these advancements, we focus on enhancing productivity in the game and animation industry through improved efficiency and streamlined production processes. By employing state-of-the-art techniques, our research enables the generation of 2D sprite images with various motions, offering significant potential for boosting productivity and creativity in the industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4255-4269
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• 2019

This paper proposes an image generation method using a Multi Discriminator Generative Adversarial Net (MDGAN) as a next generation 2D game sprite creation technique. The proposed GAN is an Autoencoder-based model that receives three areas of information-color, shape, and animation, and combines them into new images. This model consists of two encoders that extract color and shape from each image, and a decoder that takes all the values of each encoder and generates an animated image. We also suggest an image processing technique during the learning process to remove the noise of the generated images. The resulting images show that 2D sprites in games can be generated by independently learning the three image attributes of shape, color, and animation. The proposed system can increase the productivity of massive 2D image modification work during the game development process. The experimental results demonstrate that our MDGAN can be used for 2D image sprite generation and modification work with little manual cost.

• Journal of Korea Multimedia Society
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• v.10 no.10
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• pp.1310-1318
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• 2007

First of all, What was the modeling technique used to model 3D-Game character? It's a technique developed along several years, by experience... here is the bases Low polygons characters I always work in low polygon for two reasons -You can easily modify a low-poly character, change shapes, make morph for facial expressions etc -You can easily animate a low-poly character When the modeling is finished, Second, In these days, Computer hardware technologies have been bring about that expansion of various 3D digital motion pictured information and development. 3D digital techniques can be used to be diversity in Animation, Virtual-Reality, Movie, Advertisement, Game and so on. Besides, as computing power has been better and higher, the development of 3D Animations and Character are required gradually. In order to satisfy the requirement, Research about how to make 3D Game modeling that represents Character's emotions, sensibilities, is beginning to set its appearance. 3D characters in 3D Games are the core for the communications of emotion and the informations through their facial expression and characteristic motions, Sounds to Users. All concerning about 3D motion and facial expression are getting higher with extension of frequency in use. Therefore, in this study we suggest the effective method of modeling for 3D character and which are based on 2D Graphics.