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Towards Automated Garment Measurements In the Wild Using Landmark and Depth Estimation

dc.contributor.advisorDr. David Hart
dc.contributor.authorZbavitel, Cris Ian
dc.contributor.committeeMemberDr. Nic Herndon
dc.contributor.committeeMemberDr. Rui Wu
dc.contributor.departmentComputer Science
dc.date.accessioned2025-01-28T16:58:43Z
dc.date.available2025-01-28T16:58:43Z
dc.date.created2024-12
dc.date.issuedDecember 2024
dc.date.submittedDecember 2024
dc.date.updated2025-01-26T14:14:57Z
dc.degree.collegeCollege of Engineering and Technology
dc.degree.grantorEast Carolina University
dc.degree.majorMS-Computer Science
dc.degree.nameM.S.
dc.degree.programMS-Computer Science
dc.description.abstractThis research introduces an innovative approach to automate garment measurements from photos, combining depth estimation and landmark detection to address the high return rates in the fashion industry due to inaccurate sizing. Utilizing the DeepFashion2 dataset and a custom set of images, we employ DepthAnything for depth estimation and Keypoint R-CNN for landmark estimation, advancing previous methodologies by offering a scalable and accurate solution for the fashion industry. Initial findings suggest promising avenues for reducing returns and enhancing the garment fitting processes.
dc.etdauthor.orcid0009-0000-4938-4015
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10342/13834
dc.language.isoEnglish
dc.publisherEast Carolina University
dc.subjectComputer Science
dc.titleTowards Automated Garment Measurements In the Wild Using Landmark and Depth Estimation
dc.typeMaster's Thesis
dc.type.materialtext

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