Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/122354
PIRA download icon_1.1View/Download Full Text
Title: Fast multi-view consistent 3D editing with video priors
Authors: Chen, LY 
Li, RH 
Zhang, GW 
Wang, PF 
Zhang, L 
Issue Date: 2026
Source: Proceedings of the AAAI Conference on Artificial Intelligence, 2026, v. 40, no. 4, p. 2948-2956
Abstract: Text-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.
Publisher: AAAI Press
DOI: 10.1609/aaai.v40i4.37286
Description: AAAI Conference on Artificial Intelligence [AAAI-26]
Rights: Copyright © 2026, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
The 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.
Appears in Collections:Conference Paper

Files in This Item:
File Description SizeFormat 
06794-AAAI26.ChenL-CV.pdf4.33 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.