{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125661"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125661","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Building rearticulable models for 3D articulated objects from multi-view RGB videos","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01","abstract_has_math":false,"creators":["Jiang, Wei"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Wang, Shenlong"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05-24","date_published":"2024-05-24","updated_at":"2026-07-22T22:25:02Z","subjects":["3d Articulated Object Understanding","Gaussian Splatting","Neural Rendering","Part Discovery"],"languages":["en","eng"],"rights":["Copyright 2024 Wei Jiang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/125661","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wang, Shenlong"]},{"key":"dc:creator","label":"Author","values":["Jiang, Wei"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05-24","2024-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["3d Articulated Object Understanding","Gaussian Splatting","Neural Rendering","Part Discovery"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Wei Jiang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/125661"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01","The student, Wei Jiang, accepted the attached license on 2024-05-23 at 00:39.","The student, Wei Jiang, submitted this Thesis for approval on 2024-05-23 at 00:39.","This Thesis was approved for publication on 2024-05-24 at 15:40.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20810 on 2025-02-04 at 21:15:39","This thesis presents a solution to the novel task of building a 3D rearticulable models from multi-view colored (RGB) videos, which enables to render the articulable object at sampled state, discover the rigid parts, and reconstruct the kinematic structure. The solution is comprised of Gaussian Articulated Implicit Model (GAIM), a representation for dynamic scenes with an articulable object, and a relax-and-project-based optimization framework. Evaluated on the Watch-It-Move (WIM) dataset, the proposed solution achieves on par view synthesis performance as other rendering baselines, and can effectively discover rigid parts and properly reconstruct the kinematic model of the observed articulated object."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Building rearticulable models for 3D articulated objects from multi-view RGB videos"]}]}],"canonical_facts":{"dc:contributor":["Wang, Shenlong"],"dc:creator":["Jiang, Wei"],"dc:date":["2024-05-24","2024-08"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01","The student, Wei Jiang, accepted the attached license on 2024-05-23 at 00:39.","The student, Wei Jiang, submitted this Thesis for approval on 2024-05-23 at 00:39.","This Thesis was approved for publication on 2024-05-24 at 15:40.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20810 on 2025-02-04 at 21:15:39","This thesis presents a solution to the novel task of building a 3D rearticulable models from multi-view colored (RGB) videos, which enables to render the articulable object at sampled state, discover the rigid parts, and reconstruct the kinematic structure. The solution is comprised of Gaussian Articulated Implicit Model (GAIM), a representation for dynamic scenes with an articulable object, and a relax-and-project-based optimization framework. Evaluated on the Watch-It-Move (WIM) dataset, the proposed solution achieves on par view synthesis performance as other rendering baselines, and can effectively discover rigid parts and properly reconstruct the kinematic model of the observed articulated object."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/125661"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Wei Jiang"],"dc:subject":["3d Articulated Object Understanding","Gaussian Splatting","Neural Rendering","Part Discovery"],"dc:title":["Building rearticulable models for 3D articulated objects from multi-view RGB videos"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}