Identity-aware face completion for face editing applications얼굴 보정 어플리케이션을 위한 고유성 보존 인페인팅

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dc.contributor.advisorKim, Junmo-
dc.contributor.advisor김준모-
dc.contributor.authorHan, Sangeun-
dc.date.accessioned2022-04-27T19:31:04Z-
dc.date.available2022-04-27T19:31:04Z-
dc.date.issued2021-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=948993&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/295957-
dc.description학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2021.2,[iii, 15 p. :]-
dc.description.abstractExisting image inpainting methods do not utilize identity information for face completion, producing images of different identities. Considering that identity preservation is important in many real-world face editing applications, we propose a task-specific approach for identity-aware face completion, which is guided by a single reference image containing identity information. Experimental results show that our approach improves the visual quality of the completion results while preserving identity.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectface completion▼aimage inpainting▼agenerative adversarial network▼acomputer vision▼adeep learning-
dc.subject얼굴 인페인팅▼a영상 인페인팅▼a생성적 적대 신경망▼a컴퓨터 비전▼a심층 학습-
dc.titleIdentity-aware face completion for face editing applications-
dc.title.alternative얼굴 보정 어플리케이션을 위한 고유성 보존 인페인팅-
dc.typeThesis(Master)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :전기및전자공학부,-
dc.contributor.alternativeauthor한상은-
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