• Korean Journal of Computational Design and Engineering
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• v.19 no.1
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• pp.68-79
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• 2014

Human model simulation has been widely used in various industrial areas such as ergonomic design, product evaluation and characteristic analysis of work-related musculoskeletal disorders. However, the process of building digital human models and capturing their behaviors requires many costly and time-consuming fabrication iterations. To overcome the limitations of this expensive and time-consuming process, many studies have recently presented a markerless motion capture approach that reconstructs the time-varying skeletal motions from optical devices. However, the drawback of the markerless motion capture approach is that the phenomenon of occlusion of motion data occurs in real-time human simulation. In this study, we propose a systematic method of discriminating missing or inaccurate motion data due to motion occlusion and interpolating a sequence of motion frames captured by a markerless depth camera.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.345-348
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• 2020

노년층의 건강관리를 위하여 몸상태를 확인할 수 있는 심박센서와 운동량을 확인할 수 있는 모션센서 및 간단한 센서를 활용하여 실시간으로 건강분석과 관리 및 처방을 할 수 있는 방안을 제시하고 플랫폼 개발에 도전함.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.678-680
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• 2023

본 연구는 2022년 유네스코 인류무형유산 대표목록에 등재된 탈춤 동작을 디지털화하여 후속 세대에게 정보를 제공하는 것을 목적으로 한다. 데이터 수집은 국가무형문화제로 지정된 탈춤 단체 13개, 시도무형문화재 단체 5개에 소속된 무형문화재, 전승자 39명이 관성식 모션 캡처 장비를 착용하고, 8대의 카메라를 이용하여 수집하였다. 데이터 가공은 바운딩박스를 수행하였고, 탈춤동작 추정은 YOLO v8을 사용하였고 탈춤 동작 분류는 YOLO v8에 CNN모델을 결합하여 130개의 탈춤을 분류하였다. 연구결과, mAP-50은 0.953, mAP50-95는 0.596, Accuracy 70%를 달성하였다. 향후 학습용 데이터셋 구축량이 늘어나고, 데이터 품질이 개선된다면 탈춤 분류 성능은 더욱 개선될 것이라 기대한다.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.4
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• pp.231-241
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• 2006

Creating controllable, responsive avatars is an important problem in computer games and virtual environments. Recently, large collections of motion capture data have been exploited for increased realism in avatar animation and control. Large motion sets have the advantage of accommodating a broad variety of natural human motion. However, when a motion set is large, the time required to identify an appropriate sequence of motions is the bottleneck for achieving interactive avatar control. In this paper, we present a novel method for training avatar behaviors from unlabelled motion data in order to animate and control avatars at minimal runtime cost. Based on machine learning technique, called Q-teaming, our training method allows the avatar to learn how to act in any given situation through trial-and-error interactions with a dynamic environment. We demonstrate the effectiveness of our approach through examples that include avatars interacting with each other and with the user.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.5
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• pp.163-168
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• 2015

Recent exhibition's paradigm is changing from the existing unidirectional oriented exhibition form to a form of interactive hands-on exhibits that viewers can get and realistically feel a variety of information. Hands-on exhibit embodies the human interface by utilizing light, sound, pressure, etc. in time and space. In this paper, we have studied the creation of fractal image by the Mandelbrot technique and proposed the interaction method for it to be converted into a variety of forms. By using the proposed method, a variety of image transformation such as printmaking effect, sketch effect, Pop Art effect can be performed, according to clicking a certain fraction on the created fractal image screen by a user mouse. Interactive image generated in this study are expected to be used for trade shows, promotional products, media art design.