DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Noh, Junyonh | - |
dc.contributor.advisor | 노준용 | - |
dc.contributor.advisor | Lee, Sunghee | - |
dc.contributor.advisor | 이성희 | - |
dc.contributor.author | Kim, Jaehyun | - |
dc.contributor.author | 김재현 | - |
dc.date.accessioned | 2017-03-29T02:31:42Z | - |
dc.date.available | 2017-03-29T02:31:42Z | - |
dc.date.issued | 2016 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=663325&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/221350 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 문화기술대학원, 2016.8 ,[iii, 31 p. :] | - |
dc.description.abstract | Spontaneous reactions is assumed to play a vital role in making realistic human-agent or agent-agent interaction. For the spontaneity, the importance of abilities to predict action and to control reaction speed were investigated. The suggested data-driven approach used action-reaction pairs that are temporal skeleton information of two persons captured from a depth camera. The reactions synchronized with or faster than actions were made by learning the data with artificial neural networks. One part of networks predicted action pose at a time step, and the other created an interaction representation, corresponding to the action pose, which is the difference from the action pose to a reaction pose. The results showed that the synchronized and faster reaction with a few steps of valid action prediction could afford a virtual agent a certain extent of spontaneity. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | animation | - |
dc.subject | interaction | - |
dc.subject | spontaneity | - |
dc.subject | virtual character | - |
dc.subject | prediction | - |
dc.subject | 움직임 | - |
dc.subject | 상호작용 | - |
dc.subject | 자발성 | - |
dc.subject | 가상 캐릭터 | - |
dc.subject | 예측 | - |
dc.title | Study on spontaneous interactive animation for virtual agents based on iterative predictions and action-difference learning | - |
dc.title.alternative | 반복 예측 및 행동-차이 학습에 기반한 가상 캐릭터 간 자발적 상호작용 애니메이션 연구 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :문화기술대학원, | - |
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