東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Reconstructing 3D Humans from Images.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Reconstructing 3D Humans from Images./
作者:	Kolotouros, Nikolaos.
面頁冊數:	1 online resource (152 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
Contained By:	Dissertations Abstracts International84-02B.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29166078click for full text (PQDT)
ISBN:	9798837504150

Reconstructing 3D Humans from Images.
Kolotouros, Nikolaos.

Reconstructing 3D Humans from Images. - 1 online resource (152 pages)

Source: Dissertations Abstracts International, Volume: 84-02, Section: B.

Thesis (Ph.D.)--University of Pennsylvania, 2022.

Includes bibliographical references

The past decade we have seen remarkable progress in Computer Vision, fueled by the recent advances in Deep Learning. Unsurprisingly, human perception has been the center of attention. We now have access to systems that can work remarkably well for traditional 2D tasks like segmentation or pose estimation. However, scaling this to 3D remains particularly challenging because of the inherent ambiguities and the scarcity of annotations.The goal of this dissertation is to describe our contributions towards automating 3D human reconstruction from images. First, we will explore the use of different representations for human mesh recovery, discuss their advantages and show how they can be useful for learning deformations beyond standard parametric body models. Next, motivated by the limited availability of annotated data, we will present a method that leverages a collaboration between regression and optimization methods to successfully address this. Subsequently, we will describe our work on modeling the ambiguities in 3D human reconstruction and demonstrate its usefulness for solving a variety of downstream tasks, such as human body model fitting. Last, we will move beyond single-person 3D pose estimation and show how we can scale our methods to work on challenging real-world scenes with multiple humans.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798837504150Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

3DIndex Terms--Genre/Form:

542853
Electronic books.

Reconstructing 3D Humans from Images.
LDR:02657nmm a2200409K 4500 001 2358309
005 20230731112613.5
006 m o d
007 cr mn ---uuuuu
008 241011s2022 xx obm 000 0 eng d
020 $a 9798837504150
035 $a (MiAaPQ)AAI29166078
035 $a AAI29166078
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Kolotouros, Nikolaos. $3 3698847
245 1 0 $a Reconstructing 3D Humans from Images.
264 0 $c 2022
300 $a 1 online resource (152 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
500 $a Advisor: Daniilidis, Kostas.
502 $a Thesis (Ph.D.)--University of Pennsylvania, 2022.
504 $a Includes bibliographical references
520 $a The past decade we have seen remarkable progress in Computer Vision, fueled by the recent advances in Deep Learning. Unsurprisingly, human perception has been the center of attention. We now have access to systems that can work remarkably well for traditional 2D tasks like segmentation or pose estimation. However, scaling this to 3D remains particularly challenging because of the inherent ambiguities and the scarcity of annotations.The goal of this dissertation is to describe our contributions towards automating 3D human reconstruction from images. First, we will explore the use of different representations for human mesh recovery, discuss their advantages and show how they can be useful for learning deformations beyond standard parametric body models. Next, motivated by the limited availability of annotated data, we will present a method that leverages a collaboration between regression and optimization methods to successfully address this. Subsequently, we will describe our work on modeling the ambiguities in 3D human reconstruction and demonstrate its usefulness for solving a variety of downstream tasks, such as human body model fitting. Last, we will move beyond single-person 3D pose estimation and show how we can scale our methods to work on challenging real-world scenes with multiple humans.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 523869
650 4 $a Computer engineering. $3 621879
650 4 $a Bioinformatics. $3 553671
650 4 $a Morphology. $3 591167
653 $a 3D
653 $a Computer vision
653 $a Deep learning
653 $a Human pose estimation
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0984
690 $a 0464
690 $a 0800
690 $a 0715
690 $a 0287
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a University of Pennsylvania. $b Computer and Information Science. $3 2092564
773 0 $t Dissertations Abstracts International $g 84-02B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29166078 $z click for full text (PQDT)