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High quality human 3D body modeling,...

Zhang, Qing.

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High quality human 3D body modeling, tracking and application.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	High quality human 3D body modeling, tracking and application./
作者:	Zhang, Qing.
面頁冊數:	113 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-03(E), Section: B.
Contained By:	Dissertation Abstracts International77-03B(E).
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3731314
ISBN:	9781339177007

High quality human 3D body modeling, tracking and application.
Zhang, Qing.

High quality human 3D body modeling, tracking and application. - 113 p.

Source: Dissertation Abstracts International, Volume: 77-03(E), Section: B.

Thesis (Ph.D.)--University of Kentucky, 2015.

Geometric reconstruction of dynamic objects is a fundamental task of computer vision and graphics, and modeling human body of high fidelity is considered to be a core of this problem. Traditional human shape and motion capture techniques require an array of surrounding cameras or subjects wear reflective markers, resulting in a limitation of working space and portability.

ISBN: 9781339177007Subjects--Topical Terms:

523869
Computer science.

High quality human 3D body modeling, tracking and application.
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245 1 0 $a High quality human 3D body modeling, tracking and application.
300 $a 113 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-03(E), Section: B.
500 $a Adviser: Ruigang Yang.
502 $a Thesis (Ph.D.)--University of Kentucky, 2015.
520 $a Geometric reconstruction of dynamic objects is a fundamental task of computer vision and graphics, and modeling human body of high fidelity is considered to be a core of this problem. Traditional human shape and motion capture techniques require an array of surrounding cameras or subjects wear reflective markers, resulting in a limitation of working space and portability.
520 $a In this dissertation, a complete process is designed from geometric modeling detailed 3D human full body and capturing shape dynamics over time using a flexible setup to guiding clothes/person re-targeting with such data-driven models. As the mechanical movement of human body can be considered as an articulate motion, which is easy to guide the skin animation but has difficulties in the reverse process to find parameters from images without manual intervention, we present a novel parametric model, GMM-BlendSCAPE, jointly taking both linear skinning model and the prior art of BlendSCAPE (Blend Shape Completion and Animation for PEople) into consideration and develop a Gaussian Mixture Model (GMM) to infer both body shape and pose from incomplete observations. We show the increased accuracy of joints and skin surface estimation using our model compared to the skeleton based motion tracking.
520 $a To model the detailed body, we start with capturing high-quality partial 3D scans by using a single-view commercial depth camera. Based on GMM-BlendSCAPE, we can then reconstruct multiple complete static models of large pose difference via our novel non-rigid registration algorithm. With vertex correspondences established, these models can be further converted into a personalized drivable template and used for robust pose tracking in a similar GMM framework. Moreover, we design a general purpose real-time non-rigid deformation algorithm to accelerate this registration.
520 $a Last but not least, we demonstrate a novel virtual clothes try-on application based on our personalized model utilizing both image and depth cues to synthesize and retarget clothes for single-view videos of different people.
590 $a School code: 0102.
650 4 $a Computer science. $3 523869
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710 2 $a University of Kentucky. $b Computer Science. $3 3181539
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