Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/122354
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dc.contributorDepartment of Computing-
dc.creatorChen, LY-
dc.creatorLi, RH-
dc.creatorZhang, GW-
dc.creatorWang, PF-
dc.creatorZhang, L-
dc.date.accessioned2026-11-10T03:24:41Z-
dc.date.available2026-11-10T03:24:41Z-
dc.identifier.urihttp://hdl.handle.net/10397/122354-
dc.descriptionAAAI Conference on Artificial Intelligence [AAAI-26]en_US
dc.language.isoenen_US
dc.publisherAAAI Pressen_US
dc.rightsCopyright © 2026, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.en_US
dc.rightsThe following publication Chen, L., Li, R., Zhang, G., Wang, P., & Zhang, L. (2026). Fast Multi-view Consistent 3D Editing with Video Priors. Proceedings of the AAAI Conference on Artificial Intelligence, 40(4), 2948–2956 is available at https://dx.doi.org/10.1609/aaai.v40i4.37286.en_US
dc.titleFast multi-view consistent 3D editing with video priorsen_US
dc.typeConference Paperen_US
dc.identifier.spage2948-
dc.identifier.epage2956-
dc.identifier.volume40-
dc.identifier.issue4-
dc.identifier.doi10.1609/aaai.v40i4.37286-
dcterms.abstractText-driven 3D editing enables user-friendly 3D object or scene editing with text instructions. Due to the lack of multiview consistency priors, existing methods typically resort to employ 2D generation or editing models to process per-view individually, followed by iterative 2D-3D-2D updating. However, these methods are not only time-consuming but also prone to yielding over-smoothed results, since iterative process averages the different editing signals gathered from different views. In this paper, we propose, an early and pioneering work of generative Video Prior based 3D Editing, ViP3DE in short, to repurpose the temporal consistency priors from pre-trained video generation models to achieve consistent 3D editing within a single forward pass. Our key insight is to condition the video generation model on a single edited view to generate other consistent edited views for 3D updating directly, thereby bypassing iterative editing paradigm. First, 3D updating requires edited views to be paired with specific camera poses. To this end, we propose motion-preserved noise blending for the video model to generate edited views at predefined camera poses. In addition, we introduce geometrically aware denoising to further enhance multi-view consistency by integrating 3D geometric priors into video models. Extensive experiments demonstrate that our proposed ViP3DE can achieve high-quality 3D editing results even within a single forward pass, significantly outperforming existing methods in both editing quality and editing time cost.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationProceedings of the AAAI Conference on Artificial Intelligence, 2026, v. 40, no. 4, p. 2948-2956-
dcterms.issued2026-
dc.identifier.isiWOS:001767356800074-
dc.relation.conferenceThe 40th Annual AAAI Conference on Artificial Intelligence, January 20 – January 27, 2026, Singapore-
dc.description.validate202610 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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