Study on spontaneous interactive animation for virtual agents based on iterative predictions and action-difference learning = 반복 예측 및 행동-차이 학습에 기반한 가상 캐릭터 간 자발적 상호작용 애니메이션 연구

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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.
Advisors
Noh, Junyonhresearcher노준용researcherLee, Sungheeresearcher이성희researcher
Description
한국과학기술원 :문화기술대학원,
Publisher
한국과학기술원
Issue Date
2016
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 문화기술대학원, 2016.8 ,[iii, 31 p. :]

Keywords

animation; interaction; spontaneity; virtual character; prediction; 움직임; 상호작용; 자발성; 가상 캐릭터; 예측

URI
http://hdl.handle.net/10203/221350
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=663325&flag=dissertation
Appears in Collection
GCT-Theses_Master(석사논문)
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