• Cartoon and Animation Studies
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• s.41
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• pp.57-73
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• 2015

A pose is an important component in the animation with timing and spacing. Pose is the key to describe the story-telling or how the animation behavior. Key animation method is Straight Ahead and pose to pose method. Many animaters have been using these two methods, or by a mix of two ways. It is possible that computer animation make a pose using interpolation between keyframes. The many animators of computer animation are using pose to pose in their work. It is depend on good and strong pose that make audience understand a story or a situation. This makes animators to be efficient of inefficient operation. In this study, according to the effective good pose to catch proposes four ways. There are four methods of making pose that are stretch and squash, the height of the character, the center of weight, step. The law of 12 kinds of Disney Animation is a good reference for the study.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.718-727
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• 2022

The Korean domestic cartoon industry has grown explosively by 65% compared to the previous year. Then the market size is expected to exceed KRW 1 trillion. However, excessive work results in health deterioration. Moreover, this working environment makes the production of human resources insufficient, repeating a vicious cycle. Although some tasks require creation activity during cartoon production, there are still a lot of simple repetitive tasks. Therefore, this study aimed to develop a method for creating a character pose through human pose estimation (HPE). The HPE is to detect key points for each joint of a user. The primary role of the proposed method was to make each joint of the character match that of the human. The proposed method enabled us to create the pose of the two-dimensional cartoon character through the results. Furthermore, it was possible to save the static image for one character pose and the video for continuous character pose.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.465-472
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• 2022

In this paper, the style synthesis network is trained to generate style-synthesized video through the style synthesis through training Stylegan and the video synthesis network for video synthesis. In order to improve the point that the gaze or expression does not transfer stably, 3D face restoration technology is applied to control important features such as the pose, gaze, and expression of the head using 3D face information. In addition, by training the discriminators for the dynamics, mouth shape, image, and gaze of the Head2head network, it is possible to create a stable style synthesis video that maintains more probabilities and consistency. Using the FaceForensic dataset and the MetFace dataset, it was confirmed that the performance was increased by converting one video into another video while maintaining the consistent movement of the target face, and generating natural data through video synthesis using 3D face information from the source video's face.