• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.11a
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• pp.40-44
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• 1998

사용자에 게 친근감 있는 인터페이스를 제공하기 위해 자연스러운 얼굴 애니메이션에 대한 연구가 활발히 진행 중이다.[5][6] 본 논문에서는 자연스러운 얼굴의 표정 합성을 위한 애니메이션 방법 을 제안하였다. 특정한 사람을 모델로 한 얼굴 애니메이션을 위하여 우선 3차원 메쉬로 구성된 일반 모델(generic model)을 특정 사람에게 정합 하여 특정인의 3차원 얼굴 모델을 얻을 수 있다 본 논문에서는 한국인의 자연스러운 얼굴 표정합성을 위하여, 한국인의 표준얼굴에 관한 연구결과를 토대로 한국인 얼굴의 특징을 반영한 일반모델을 만들고 이를 이용하여 특정인의 3차원 얼굴 모델을 얻을 수 있도록 하였다. 실제 얼굴의 근육 및 피부 조직 등 해부학적 구조에 기반 한 표정 합성방법을 사용하여 현실감 있고 자연스러운 얼굴 애니메이션이 이루어질 수 있도록 하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04a
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• pp.742-744
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• 2002

얼굴 애니메이션은 개인을 쉽게 구분하고 의사소통을 효율적으로 할 수 있는 보조도구로써 최근 연구가 활발하다. 본 논문에서는 얼굴 표정생성을 위해서 실제얼굴의 피부조직 얼굴 근육 등 해부학적 구조에 기반한 근육 기반 모델 방법을 사용하여 현실감 있고 자연스러운 얼굴 애니메이션이 이루어지도록 한다. 또한 부드러운 얼굴모델을 구현하기 위하여 폴리곤 메쉬를 분할하고 얼굴 표정에 중요한 영향을 미치는 얼굴근육을 추가하여 다양하고 자연스러운 표정을 연출하는 방법을 제시하고자 한다. 제안된 방법을 water〔3〕의 모델에 적용해 봄으로서 더 실감 있는 얼굴 애니메이션에 접근할 수 있는 결과를 얻을 수 있었다. 이 결과는 화상회의나 가상현실, 원격교육, 영화 등 많은 분야에서 활용될 수 있다.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.311-320
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• 2007

This paper presents vision-based 3D facial expression animation technique and system which provide the robust 3D head pose estimation and real-time facial expression control. Many researches of 3D face animation have been done for the facial expression control itself rather than focusing on 3D head motion tracking. However, the head motion tracking is one of critical issues to be solved for developing realistic facial animation. In this research, we developed an integrated animation system that includes 3D head motion tracking and facial expression control at the same time. The proposed system consists of three major phases: face detection, 3D head motion tracking, and facial expression control. For face detection, with the non-parametric HT skin color model and template matching, we can detect the facial region efficiently from video frame. For 3D head motion tracking, we exploit the cylindrical head model that is projected to the initial head motion template. Given an initial reference template of the face image and the corresponding head motion, the cylindrical head model is created and the foil head motion is traced based on the optical flow method. For the facial expression cloning we utilize the feature-based method, The major facial feature points are detected by the geometry of information of the face with template matching and traced by optical flow. Since the locations of varying feature points are composed of head motion and facial expression information, the animation parameters which describe the variation of the facial features are acquired from geometrically transformed frontal head pose image. Finally, the facial expression cloning is done by two fitting process. The control points of the 3D model are varied applying the animation parameters to the face model, and the non-feature points around the control points are changed by use of Radial Basis Function(RBF). From the experiment, we can prove that the developed vision-based animation system can create realistic facial animation with robust head pose estimation and facial variation from input video image.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05c
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• pp.465-468
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• 2002

최근 컴퓨터를 이용한 시각 분야가 발전하면서 인간과 관계된 연구가 중요시 되어, 사람과 컴퓨터의 인터페이스에 대한 새로운 시도들이 다양하게 이루어지고 있다. 특히 얼굴 형상 모델링과 얼굴 표정변화를 애니메이션 화하는 방법에 대한 연구가 활발히 수행되고 있으며, 그 용도가 매우 다양하고, 적용 범위도 증가하고 있다. 본 논문에서는 한국인의 얼굴특성에 맞는 표준적인 일반모델을 생성하고, 실제 사진과 같이 개개인의 특성에 따라 정확한 형상을 유지할 수 있는 3차원 형상 모델을 제작한다. 그리고 자연스러운 얼굴 표정 생성을 위하여, 근육모델 기반의 얼굴표정 생성 시스템을 개발하여, 자연스럽고 실제감 있는 얼굴애니메이션이 이루어질 수 있도록 하였다.

• The KIPS Transactions:PartA
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• v.14A no.2
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• pp.85-90
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• 2007

This paper presents a facial animation and expression control method that enables the animator to select any facial frames from the facial expression space, whose expression transfer paths the system can setup automatically. Our system creates the facial expression space from approximately 2500 captured facial frames. To create the facial expression space, we get distance between pairs of feature points on the face and visualize the space of expressions in 2D space by using the Multidimensional scaling(MDS). To setup most suitable expression transfer paths, we classify the facial expression space into four field on the basis of any facial expression state. And the system determine the state of expression in the shortest distance from every field, then the system transfer from the state of any expression to the nearest state of expression among thats. To complete setup, our system continue transfer by find second, third, or fourth near state of expression until finish. If the animator selects any key frames from facial expression space, our system setup expression transfer paths automatically. We let animators use the system to create example animations or to control facial expression, and evaluate the system based on the results.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.117-124
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• 2007

This paper describes methodology that enables animators to create the facial expression animations and to control the facial expressions in real-time by reusing motion capture datas. In order to achieve this, we fix a facial expression state expression method to express facial states based on facial motion data. In addition, by distributing facial expressions into intuitive space using LLE algorithm, it is possible to create the animations or to control the expressions in real-time from facial expression space using user interface. In this paper, approximately 2400 facial expression frames are used to generate facial expression space. In addition, by navigating facial expression space projected on the 2-dimensional plane, it is possible to create the animations or to control the expressions of 3-dimensional avatars in real-time by selecting a series of expressions from facial expression space. In order to distribute approximately 2400 facial expression data into intuitional space, there is need to represents the state of each expressions from facial expression frames. In order to achieve this, the distance matrix that presents the distances between pairs of feature points on the faces, is used. In order to distribute this datas, LLE algorithm is used for visualization in 2-dimensional plane. Animators are told to control facial expressions or to create animations when using the user interface of this system. This paper evaluates the results of the experiment.

• Journal of the Korea Computer Industry Society
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• v.4 no.4
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• pp.569-578
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• 2003

This paper presents a method in which the user produces a real-time facial animation by navigating in the space of facial expressions created from a great number of captured facial expressions. The core of the method is define the distance between each facial expressions and how to distribute into suitable intuitive space using it and user interface to generate realtime facial expression animation in this space. We created the search space from about 2,400 raptured facial expression frames. And, when the user free travels through the space, facial expressions located on the path are displayed in sequence. To visually distribute about 2,400 captured racial expressions in the space, we need to calculate distance between each frames. And we use Floyd's algorithm to get all-pairs shortest path between each frames, then get the manifold distance using it. The distribution of frames in intuitive space apply a multi-dimensional scaling using manifold distance of facial expression frames, and distributed in 2D space. We distributed into intuitive space with keep distance between facial expression frames in the original form. So, The method presented at this paper has large advantage that free navigate and not limited into intuitive space to generate facial expression animation because of always existing the facial expression frames to navigate by user. Also, It is very efficient that confirm and regenerate nth realtime generation using user interface easy to use for facial expression animation user want.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.163-167
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• 2009

본 논문에서는 크게 2단계에 걸쳐 다양한 형태의 얼굴을 가진 2차원 애니메이션 상의 캐릭터 얼굴구성요소를 추출하고 표정을 분석한다. 첫 번째 단계에서는, 기존의 얼굴인식 및 표정인식 분야에서 이용되었던 동적메쉬모델을 간소화하여 캐릭터 얼굴에 적용하기 위한 최적의 표준 메쉬모델을 제작하고, 이 모델을 사용하여 얼굴구성요소의 위치 및 형태정보를 추출한다. 두 번째 단계에서는, 앞 단계에서 추출된 3가지 얼굴구성요소(눈썹, 눈, 입)를 사용하여 FACS(Facial Action Coding System)에 정의된 AU(Action Units) 44개 중 12개의 AU를 사용하여 캐릭터의 5까지 기본적인 얼굴 표정에 대해 분석 및 정의한다. 본 논문에서 정의한 AU로 기본적인 5가지 얼굴표정에 대해 표정분석 정확도를 측정하였고, 서로 다른 캐릭터에 실험함으로써 제안된 AU정의의 타당성을 제시한다.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.795-802
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• 2005

In this paper, we present a facial expression recognition-synthesis system that recognizes 7 basic emotion information automatically and renders face with non-photorelistic style in PDA For the recognition of the facial expressions, first we need to detect the face area within the image acquired from the camera. Then, a normalization procedure is applied to it for geometrical and illumination corrections. To classify a facial expression, we have found that when Gabor wavelets is combined with enhanced Fisher model the best result comes out. In our case, the out put is the 7 emotional weighting. Such weighting information transmitted to the PDA via a mobile network, is used for non-photorealistic facial expression animation. To render a 3-D avatar which has unique facial character, we adopted the cartoon-like shading method. We found that facial expression animation using emotional curves is more effective in expressing the timing of an expression comparing to the linear interpolation method.

• The KIPS Transactions:PartA
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• v.11A no.2
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• pp.189-194
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• 2004

This paper describes method to distribute much high-dimensional facial expression motion data to 2 dimensional space, and method to create facial expression animation by select expressions that want by realtime as animator navigates this space. In this paper composed expression space using about 2400 facial expression frames. The creation of facial space is ended by decision of shortest distance between any two expressions. The expression space as manifold space expresses approximately distance between two points as following. After define expression state vector that express state of each expression using distance matrix which represent distance between any markers, if two expression adjoin, regard this as approximate about shortest distance between two expressions. So, if adjacency distance is decided between adjacency expressions, connect these adjacency distances and yield shortest distance between any two expression states, use Floyd algorithm for this. To materialize expression space that is high-dimensional space, project on 2 dimensions using Sammon's Mapping. Facial animation create by realtime with animators navigating 2 dimensional space using user interface.