• 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.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.336-343
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• 2021

AI technology is spreading not only to industrial fields, but also to culture, art, and content fields. In this paper, we proposed a system based on AI technology that can automate the process of reproducing contents using characters for movie contents. After creating the basic appearance of the character by using the StyleGAN2 model from the video extracted from the movie contents, analyzing the character's personality and propensity using the extracted dialogue data, it was determined from the contemplative appearance based on the yin-yang and five elements to the character's propensity. Accordingly, the external characteristics are reflected in the character. Using the OpenPose model, a character's motion is created, and the finally generated data is integrated to reproduce the content. It is expected that many movie contents can be reproduced through the study of the proposed system.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.489-494
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• 2017

In spite of lots of differences in the production process of film animation, the animating method through computer is not much different between game animation and film animation. It is because the principles of film animation are also emphasized for the production of game animation. This study aims to consider the line of action showing the direction of movement and flow of energy among many considerations for the character pose work in case when producing game animation. Starting from the basic theory of drawing, the line of action plays a role of bible in the pose work including cell animation and 3D animation. After examining the theoretical background through preceding researches in order to understand the application of the line of action to the pose work of game characters, the poses of hero characters of a 3D online game 'Over Watch' were collected and then lines were directly drawn to analyze them. And as a result, the pose of characters with simple and clear 'Line of action' was good. This study aimed to consider the expression techniques of character pose in the production of game animation, which is expected to be used as an important reference for game animators at work.

• 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.

• The Journal of the Korea Contents Association
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• v.10 no.9
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• pp.1-8
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• 2010

In this paper, we propose a large scale entertainment system based on gesture recognition for learning Chinese character contents. The system is consisted of parts that forecast user's posture in two infrared images and part that recognize gestures from continuous poses. And we can divide and acquire in front side pose and side pose about one pose in each IR camera. This entertainment system is immersive in nature and convenient for its gestures based controlling system. Also, it can maximize information transmission because induce immersion and interest using two large size displays and various multimedia elements. The learning Chinese character contents can master Chinese character naturally because give interest to user and supply game and education at the same time. Therefore, it can expect synergy effect that can learn playing to user combining with large entertainment system based on gesture recognition.

• Journal of the Korea Computer Graphics Society
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• v.25 no.3
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• pp.85-92
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• 2019

A 3D virtual character playing a role in a digital story-telling has a unique style in its appearance and motion. Because the style reflects the unique personality of the character, it is very important to preserve the style and keep its consistency. However, when the character's motion is directly controlled by a user's motion who is wearing motion sensors, the unique style can be discarded. We present a novel character motion control method that uses only a small amount of animation data created only for the character to preserve the style of the character motion. Instead of machine learning approaches requiring a large amount of training data, we suggest a search-based method, which directly searches the most similar character pose from the animation data to the current user's pose. To show the usability of our method, we conducted our experiments with a character model and its animation data created by an expert designer for a virtual reality game. To prove that our method preserves well the original motion style of the character, we compared our result with the result obtained by using general human motion capture data. In addition, to show the scalability of our method, we presented experimental results with different numbers of motion sensors.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.611-615
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• 2010

In this paper, we propose a method that exaggerate a character motion using quadratic deformation. While the previous methods tend to exaggerate a rotational motion of an individual joint angle, our method attempt to model the poses of a whole body at each frame and exaggerate those, so that the whole-pose action of the character can be exaggerated. Our method can be computed in real-time, and prevents a joint motion that rotates unexpected direction.

• Journal of the Korea Computer Graphics Society
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• v.26 no.2
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• pp.11-19
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• 2020

When a character's pose changes, its center of mass(COM) also changes. The change of COM has distinctive patterns corresponding to various motion types like walking, running or sitting. Thus the motion type can be predicted by using COM movement. We propose a motion generator that uses character's center of mass information. This generator can generate various motions without annotated action type labels. Thus dataset for training and running can be generated full-automatically. Our neural network model takes the motion history of the character and its center of mass information as inputs and generates a full-body pose for the current frame, and is trained using simple Convolutional Neural Network(CNN) that performs 1D convolution to deal with time-series motion data.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1321-1330
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• 2023

This study attempts to address the problem of 3D pose estimation for multiple human objects through a single image generated during the character development process that can be used in augmented reality. In the existing top-down method, all objects in the image are first detected, and then each is reconstructed independently. The problem is that inconsistent results may occur due to overlap or depth order mismatch between the reconstructed objects. The goal of this study is to solve these problems and develop a single network that provides consistent 3D reconstruction of all humans in a scene. Integrating a human body model based on the SMPL parametric system into a top-down framework became an important choice. Through this, two types of collision loss based on distance field and loss that considers depth order were introduced. The first loss prevents overlap between reconstructed people, and the second loss adjusts the depth ordering of people to render occlusion inference and annotated instance segmentation consistently. This method allows depth information to be provided to the network without explicit 3D annotation of the image. Experimental results show that this study's methodology performs better than existing methods on standard 3D pose benchmarks, and the proposed losses enable more consistent reconstruction from natural images.

• Journal of the Korea Computer Graphics Society
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• v.21 no.1
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• pp.23-31
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• 2015

We present a method for creating realistic 2D stick figure animations easily and rapidly using captured motion data. Stick figure animations are typically created by drawing a single pose for each frame manually for the entire time interval. In contrast, our method allows the user to summarize an action (e.g. kick, jump) for an extended period of time into a single image in which one or more action lines are drawn over a stick figure to represent the moving directions of body parts. In order to synthesize a series of time-varying poses automatically from the given image, our system first builds a deformable character model that can make arbitrary deformations of the user's stick figure drawing in 2D plane. Then, the system searches for an optimal motion segment that best fits the given pose and action lines from pre-recorded motion database. Deforming the character model to imitate the retrieved motion segment produces the final stick figure animation. We demonstrate the usefulness of our method in creating interesting stick figure animations with little effort through experiments using a variety of stick figure styles and captured motion data.