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Data-driven Approaches for Personalized Head Reconstruction
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Data-driven Approaches for Personalized Head Reconstruction
자료유형  
 학위논문
Control Number  
0014999153
International Standard Book Number  
9780438176843
Dewey Decimal Classification Number  
004
Main Entry-Personal Name  
Liang, Shu.
Publication, Distribution, etc. (Imprint  
[Sl] : University of Washington, 2018
Publication, Distribution, etc. (Imprint  
Ann Arbor : ProQuest Dissertations & Theses, 2018
Physical Description  
111 p
General Note  
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
General Note  
Adviser: Linda G. Shapiro.
Dissertation Note  
Thesis (Ph.D.)--University of Washington, 2018.
Summary, Etc.  
요약Personalized 3D face reconstruction has produced exciting results over the past few years. However, traditional methods usually require complicated setups or controlled environments to get the detailed shape of a person's face. Most methods focu
Summary, Etc.  
요약The first part of our work introduces an algorithm that takes a single frame of a person's face from a commercial depth camera Kinect and produces a high-resolution 3D mesh of the input leveraging a large research dataset of 3D face meshes. We d
Summary, Etc.  
요약In order to free people from the capturing session, the larger portion of this thesis focuses on reconstructing not only the face, but also the rest of the head using in-the-wild image collections and videos. We first introduce a boundary-value
Summary, Etc.  
요약Results on photos of celebrities downloaded from the Internet are given. However, in this algorithm, we have not reconstructed a complete head model and a specific model of the hair is lacked.
Summary, Etc.  
요약We further utilize a person's in-the-wild video to recover the full head model considering the multi-view information and hairstyle consistency across video frames. Given a video of a person's head, e.g., a TV interview, our method automatically
Subject Added Entry-Topical Term  
Computer science
Added Entry-Corporate Name  
University of Washington Computer Science and Engineering
Host Item Entry  
Dissertation Abstracts International. 79-12B(E).
Host Item Entry  
Dissertation Abstract International
Electronic Location and Access  
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Control Number  
joongbu:557331
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