• 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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.156-157
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• 2022

본 논문에서는 선택적 시점에서의 2D 포즈 추정(pose estimation) 결과를 정합 하여 정확도 높은 3D 스켈레톤(skeleton)을 만들어 낸다. 여러 프레임의 3D 데이터를 10 도 간격으로 36 방향에서 투영한 뒤, 2D 포즈 추정 결과 신뢰도가 높은 시점에서의 결과만을 선별하여 3 차원으로 정합 한다. 이때 사용하는 시점의 개수를 달리하며 정확도에 미치는 영향을 분석하여 실험적으로 정확도가 높은 최소의 시점 개수를 정하였다. 또한, 정합 한 3D 뼈대를 모션 캡쳐(motion capture) 센서와 비교하여 제안하는 알고리즘에 의해 3D 포즈 추정의 정확도가 향상되는 것을 확인했다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.77-84
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• 2012

Face classification has wide applications in intelligent video surveillance, content retrieval, robot vision, and human-machine interface. Pose and expression changes, and arbitrary illumination are typical problems for face recognition. When the face is captured at a distance, the image quality is often degraded by blurring and noise corruption. This paper investigates the efficacy of multi-classifier decision level fusion for face classification based on the photon-counting linear discriminant analysis with two different cost functions: Euclidean distance and negative normalized correlation. Decision level fusion comprises three stages: cost normalization, cost validation, and fusion rules. First, the costs are normalized into the uniform range and then, candidate costs are selected during validation. Three fusion rules are employed: minimum, average, and majority-voting rules. In the experiments, unfocusing and motion blurs are rendered to simulate the effects of the long distance environments. It will be shown that the decision-level fusion scheme provides better results than the single classifier.