• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1999.11a
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• pp.368-372
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• 1999

새롭게 표준화된 멀티미디어 동영상 파일 포맷인 MPEG-4에는 자연영상과 소리뿐만 아니라 합성된 그래픽과 소리를 포함하고 있다. 특히 화상회의나 가상환경의 아바타를 구성하기 위한 모델링과 에니메이션을 위한 FDP, FAP에 대한 표준안을 포함하고 있다. 본 논문은 MPEG-4에서 정의한 FDP와 FAP를 이용하여 화상회의나 가상환경의 아바타로 자연스럽고 현실감 있게 사용할 수 있는 얼굴 모델 생성을 위해서 보다 정교한 일반모델을 사용하고, 이에 근육 모델을 사용하여 보다 정밀한 표정 생성을 위해서 임의의 위치에 근육을 생성 할 수 있도록 근육 편집기를 작성하여, 표정 에니메이션을 수행할 수 있도록 에니메이션 편집 프로그램을 구현하였다.

• Journal of the Korea Computer Graphics Society
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• v.3 no.2
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• pp.27-34
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• 1997

3-D image modeling is in high demand for automated visual inspection and non-destructive testing. It also can be useful in biomedical research, medical therapy, surgery planning, and simulation of critical surgery (i.e. cranio-facial). Image processing and image analysis are used to enhance and classify medical volumetric data. Analyzing medical volumetric data is very difficult In this paper, we propose a new image modeling method based on tetrahedrization to improve the visualization of three-dimensional medical volumetric data. In this method, the trivariate piecewise linear interpolation is applied through the constructed tetrahedral domain. Also, visualization methods including iso-surface, color contouring, and slicing are discussed. This method can be useful to the correct and speedy analysis of medical volumetric data, because it doesn't have the ambiguity problem of Marching Cubes algorithm and achieves the data reduction. We expect to compensate the degradation of an accuracy by using an adaptive sub-division of tetrahedrization based on least squares fitting.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.51-52
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• 2018

사실감 있는 디지털 캐릭터를 만드는 것은 3D 영역에서 가장 어려운 도전 중에 하나이다. 특히 얼굴의 특징을 만들기 어렵기 때문에 얼굴포착기술이 점점 보편화되어가고 있다. 본문에서도 Photo Scan의 얼굴포착으로 고품질의 디지털 캐릭터 모델을 얻는 방법을 제시하였다. 이 방법은 시간을 단축시킬 수 있을 뿐 아니라 효율이 높아서 이 작업 과정이 고품질의 디지털 캐릭터 모델을 얻는 데 매우 유용하다.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.129-134
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• 2009

Recently, percentage of people who use SMS service is increasing. However, it is difficult to express own complicated emotion with text and emoticon of exited SMS service. This paper focuses on that point and practical uses character animation to express emotion and nuance correctly, funny. Also this paper suggests emotion based gesture animation generation system that use character's facial expression and gesture to delivery emotion excitably and clearly than only speaking. Michel[1] investigated interview movies of a person whose gesturing style they wish to animate and suggested gesture generation graph for stylized gesture animation. In this paper, we make focus to analyze and abstracted emotional gestures of Disney animation characters and did 3D modeling of these emotional gestures expanding Michel[1]'s research. To express emotion of person, suggests a emotion gesture generation graph that reflects emotion flow graph express emotion flow for probability. We investigated user reaction for research the propriety of suggested system and alternation propriety.

• Journal of KIISE:Software and Applications
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• v.30 no.1_2
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• pp.153-163
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• 2003

In this paper, we propose a new technique of image based rendering(IBR) for the pose transformation of a face by using only a frontal face image and its mesh without a three-dimensional model. To substitute the 3D geometric model, first, we make up a standard mesh set of a certain person for several face sides ; front. left, right, half-left and half-right sides. For the given person, we compose only the frontal mesh of the frontal face image to be transformed. The other mesh is automatically generated based on the standard mesh set. And then, the frontal face image is geometrically transformed to give different view by using Invertible Meshwarp Algorithm, which is improved to tolerate the overlap or inversion of neighbor vertexes in the mesh. The same warping algorithm is used to generate the opening or closing effect of both eyes and a mouth. To evaluate the transformation performance, we capture dynamic images from 10 persons rotating their heads horizontally. And we measure the location error of 14 main features between the corresponding original and transformed facial images. That is, the average difference is calculated between the distances from the center of both eyes to each feature point for the corresponding original and transformed images. As a result, the average error in feature location is about 7.0% of the distance from the center of both eyes to the center of a mouth.