{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129198"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129198","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Uni4D: Unifying visual foundation models for 4D modeling from a single video","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Yao, David Yifan"],"institution":"University of Illinois 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":2025,"date_issued":"2025-04-15","date_published":"2025-04-15","updated_at":"2026-07-22T22:25:04Z","subjects":["Computer Vision","Machine Learning","Structure from Motion","4D Reconstruction","Dynamic Modeling","Video Depth Estimation","Pose Estimation"],"languages":["en","eng"],"rights":["Copyright 2025 David Yifan Yao"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129198","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":["Yao, David Yifan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-15","2025-05"]},{"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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Vision","Machine Learning","Structure from Motion","4D Reconstruction","Dynamic Modeling","Video Depth Estimation","Pose Estimation"]}]},{"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 2025 David Yifan Yao"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129198"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, David Yao, accepted the attached license on 2025-04-13 at 23:59.","The student, David Yao, submitted this Thesis for approval on 2025-04-14 at 00:06.","This Thesis was approved for publication on 2025-04-15 at 05:27.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21758 on 2025-10-19 at 18:09:21","This paper presents a unified approach to understanding dynamic scenes from casual videos. Large pretrained vision foundation models, such as vision-language, video depth prediction, motion tracking, and segmentation models, offer promising capabilities. However, training a single model for comprehensive 4D understanding remains challenging. We introduce Uni4D, a multi-stage optimization framework that harnesses multiple pretrained models to advance dynamic 3D modeling, including static/dynamic reconstruction, camera pose estimation, and dense 3D motion tracking. Our results show state-of-the-art performance in dynamic 4D modeling with superior visual quality. Notably, Uni4D requires no retraining or fine-tuning, highlighting the effectiveness of repurposing visual foundation models for 4D understanding."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Uni4D: Unifying visual foundation models for 4D modeling from a single video"]}]}],"canonical_facts":{"dc:contributor":["Wang, Shenlong"],"dc:creator":["Yao, David Yifan"],"dc:date":["2025-04-15","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, David Yao, accepted the attached license on 2025-04-13 at 23:59.","The student, David Yao, submitted this Thesis for approval on 2025-04-14 at 00:06.","This Thesis was approved for publication on 2025-04-15 at 05:27.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21758 on 2025-10-19 at 18:09:21","This paper presents a unified approach to understanding dynamic scenes from casual videos. Large pretrained vision foundation models, such as vision-language, video depth prediction, motion tracking, and segmentation models, offer promising capabilities. However, training a single model for comprehensive 4D understanding remains challenging. We introduce Uni4D, a multi-stage optimization framework that harnesses multiple pretrained models to advance dynamic 3D modeling, including static/dynamic reconstruction, camera pose estimation, and dense 3D motion tracking. Our results show state-of-the-art performance in dynamic 4D modeling with superior visual quality. Notably, Uni4D requires no retraining or fine-tuning, highlighting the effectiveness of repurposing visual foundation models for 4D understanding."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129198"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 David Yifan Yao"],"dc:subject":["Computer Vision","Machine Learning","Structure from Motion","4D Reconstruction","Dynamic Modeling","Video Depth Estimation","Pose Estimation"],"dc:title":["Uni4D: Unifying visual foundation models for 4D modeling from a single video"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:04Z"}