FaceSyncNet : a deep learning-based approach for non-linear synchronization of facial performance videos = 얼굴 표정 연기 비디오의 비선형 동기화를 위한 심층 학습 기반 접근법a deep learning-based approach for non-linear synchronization of facial performance videos

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dc.contributor.advisorLee, Sung-Hee-
dc.contributor.advisor이성희-
dc.contributor.authorCho, Yoonjae-
dc.date.accessioned2021-05-12T19:36:54Z-
dc.date.available2021-05-12T19:36:54Z-
dc.date.issued2020-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=910812&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/284019-
dc.description학위논문(석사) - 한국과학기술원 : 문화기술대학원, 2020.2,[iii, 23 p. :]-
dc.description.abstractGiven a pair of facial performance videos, we present a deep learning-based approach that can automatically return a synchronized version of these videos. Traditional methods require precise facial landmark tracking and/or clean audio, and thus are sensitive to tracking inaccuracies and audio noise. To alleviate these issues, our approach leverages large-scale video datasets along with their associated audio tracks and trains a deep learning network to learn the audio descriptors of video frames. We then use these descriptors to compute the similarity between video frames in a cost matrix and compute a low-cost non-linear synchronization path. Both quantitative and qualitative evaluations have shown that our approach outperforms existing state-of-the-art methods.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectVideo synchronization▼aDeep learning▼aFacial performance video▼aAudio visual information-
dc.subject비디오 동기화▼a딥러닝▼a얼굴 표정 연기 비디오▼a음성-시각 정보-
dc.titleFaceSyncNet : a deep learning-based approach for non-linear synchronization of facial performance videos = 얼굴 표정 연기 비디오의 비선형 동기화를 위한 심층 학습 기반 접근법-
dc.title.alternativea deep learning-based approach for non-linear synchronization of facial performance videos-
dc.typeThesis(Master)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :문화기술대학원,-
dc.contributor.alternativeauthor조윤재-
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