• 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.

• Journal of Korea Multimedia Society
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• v.12 no.8
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• pp.1128-1141
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• 2009

Graphical expressions are continuously improving, spurred by the astonishing growth of the game technology industry. Despite such improvements, users are still demanding a more natural gaming environment and true reflections of human emotions. In real life, people can read a person's moods from facial color and expression. Hence, interactive facial colors in game characters provide a deeper level of reality. In this paper we propose a facial color adaptive technique, which is a combination of an emotional model based on human emotion theory, emotional expression pattern using colors of animation contents, and emotional reaction speed function based on human personality theory, as opposed to past methods that expressed emotion through blood flow, pulse, or skin temperature. Experiments show this of expression of the Facial Color Model based on facial color adoptive technique and expression of the animation contents is effective in conveying character emotions. Moreover, the proposed Facial Color Adaptive Technique can be applied not only to 2D games, but to 3D games as well.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.12
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• pp.726-734
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• 2004

This paper presents a facial animation method that enables the user to select a sequence of facial frames from the facial expression space, whose level of details the user can select hierarchically Our system creates the facial expression space from about 2400 captured facial frames. To represent the state of each expression, we use the distance matrix that represents the distance between pairs of feature points on the face. The shortest trajectories are found by dynamic programming. The space of facial expressions is multidimensional. To navigate this space, we visualize the space of expressions in 2D space by using the multidimensional scaling(MDS). But because there are too many facial expressions to select from, the user faces difficulty in navigating the space. So, we visualize the space hierarchically. To partition the space into a hierarchy of subspaces, we use fuzzy clustering. In the beginning, the system creates about 10 clusters from the space of 2400 facial expressions. Every tine the level increases, the system doubles the number of clusters. The cluster centers are displayed on 2D screen and are used as candidate key frames for key frame animation. The user selects new key frames along the navigation path of the previous level. At the maximum level, the user completes key frame specification. We let animators use the system to create example animations, and evaluate the system based on the results.

• Journal of Internet Computing and Services
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• v.6 no.1
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• pp.85-93
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• 2005

This paper describes how to distribute high multi-dimensional facial expression data of vast quantity over a suitable space and produce facial expression animations by selecting expressions while animator navigates this space in real-time. We have constructed facial spaces by using about 2400 facial expression frames on this paper. These facial spaces are created by calculating of the shortest distance between two random expressions. The distance between two points In the space of expression, which is manifold space, is described approximately as following; When the linear distance of them is shorter than a decided value, if the two expressions are adjacent after defining the expression state vector of facial status using distance matrix expressing distance between two markers, this will be considered as the shortest distance (manifold distance) of the two expressions. Once the distance of those adjacent expressions was decided, We have taken a Floyd algorithm connecting these adjacent distances to yield the shortest distance of the two expressions. We have used CCA(Curvilinear Component Analysis) technique to visualize multi-dimensional spaces, the form of expressing space, into two dimensions. While the animators navigate this two dimensional spaces, they produce a facial animation by using user interface in real-time.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.16.1-164
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• 1998

This paper describes Facial Expression Explorer to search for the components of a facial expression and to map the expression to other expressionless figures like a robot, frog, teapot, rabbit and others. In general, it is a time-consuming and laborious job to create a facial expression manually, especially when the facial expression must personify a well-known public figure or an actor. In order to extract a blending ratio from facial images automatically, the Facial Expression Explorer uses Networked Genetic Algorithm(NGA) which is a fast method for the convergence by GA. The blending ratio is often used to create facial expressions through shape blending methods by animators. With the Facial Expression Explorer a realistic facial expression can be modeled more efficiently.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.2
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• pp.131-136
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• 2004

According to development of media, various information is recorded in media, expression is one during interesting information. Because expression includes of relationship of human inside. Intention of inside is expressed by gesture, but expression has more information. And, expression can manufacture voluntarily, include plan of inside on the man. Also, expression has unique character in a person, have alliance that do division possibility. In this paper, to analyze expression of USB camera animation, wish to detect facial building block. Because characteristic point by person's expression change exists on face component. For component detection, in animation one frame with Capture, grasp facial position, and separate face area, and detect characteristic points of face component.

• Journal of the Korea Computer Graphics Society
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• v.18 no.1
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• pp.23-28
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• 2012

In this paper, we present a new method for automatically synchronize captured facial expression data with its corresponding motion data. In a usual optical motion capture set-up, a detailed facial expression can not be captured simultaneously in the motion capture session because its resolution requirement is higher than that of the motion capture. Therefore, those are captured in two separate sessions and need to be synchronized in the post-process to be used for generating a convincing character animation. Based on the patterns of the actor's neck movement extracted from those two data, we present a non-linear time warping method for the automatic synchronization. We justify our method with the actual examples to show the viability of the method.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.16-25
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• 2018

The talking head (TH) indicates an utterance face animation generated based on text and voice input. In this paper, we propose the generation method of TH with facial expression and intonation by speech input only. The problem of generating TH from speech can be regarded as a regression problem from the acoustic feature sequence to the facial code sequence which is a low dimensional vector representation that can efficiently encode and decode a face image. This regression was modeled by bidirectional RNN and trained by using SAVEE database of the front utterance face animation database as training data. The proposed method is able to generate TH with facial expression and intonation TH by using acoustic features such as MFCC, dynamic elements of MFCC, energy, and F0. According to the experiments, the configuration of the BLSTM layer of the first and second layers of bidirectional RNN was able to predict the face code best. For the evaluation, a questionnaire survey was conducted for 62 persons who watched TH animations, generated by the proposed method and the previous method. As a result, 77% of the respondents answered that the proposed method generated TH, which matches well with the speech.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.13-19
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• 2023

In this paper, we investigated the implementation of a hairstyle recommendation system based on big data and deepfake technology. The proposed hairstyle recommendation system recognizes the facial shapes based on the user's photo (image). Facial shapes are classified into oval, round, and square shapes, and hairstyles that suit each facial shape are synthesized using deepfake technology and provided as videos. Hairstyles are recommended based on big data by applying the latest trends and styles that suit the facial shape. With the image segmentation map and the Motion Supervised Co-Part Segmentation algorithm, it is possible to synthesize elements between images belonging to the same category (such as hair, face, etc.). Next, the synthesized image with the hairstyle and a pre-defined video are applied to the Motion Representations for Articulated Animation algorithm to generate a video animation. The proposed system is expected to be used in various aspects of the beauty industry, including virtual fitting and other related areas. In future research, we plan to study the development of a smart mirror that recommends hairstyles and incorporates features such as Internet of Things (IoT) functionality.

• Journal of the Korea Society of Computer and Information
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• v.17 no.8
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• pp.31-40
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• 2012

In this paper, we propose a method of synthesizing faces of a user and an animation character using a 3D face model. The suggested method first receives two orthogonal 2D face images and extracts major features of the face through the template snake. It then generates a user-customized 3D face model by adjusting a generalized face model using the extracted facial features and by mapping texture maps obtained from two input images to the 3D face model. Finally, it generates a user-customized animation character by synthesizing the generated 3D model to an animation character reflecting the position, size, facial expressions, and rotational information of the character. Experimental results show some results to verify the performance of the suggested algorithm. We expect that our method will be useful to various applications such as games and animation movies.