Fast Multi-view Consistent 3D Editing with Video Priors
Liyi Chen, Ruihuang Li, Guowen Zhang, Pengfei Wang, Lei Zhang
摘要
Text-driven 3D editing enables user-friendly 3D object or scene editing with text instructions. Due to the lack of multi-view 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.
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引用它的顶会 Paper5
- One2Scene: Geometric Consistent Explorable 3D Scene Generation from a Single ImagePengfei Wang, Liyi Chen, Zhiyuan Ma, Yanjun Guo 等ICLR 2026 · 被引用 11 次
- Omni-3DEdit: Generalized Versatile 3D Editing in One-PassLiyi Chen, Pengfei Wang, Guowen Zhang, Zhiyuan Ma 等CVPR 2026 · 被引用 6 次
- EffectMaker: Unifying Reasoning and Generation for Customized Visual Effect CreationShiyuan Yang, Ruihuang Li, Jiale Tao, Shuai Shao 等CVPR 2026 · 被引用 2 次
- BEVDilation: LiDAR-Centric Multi-Modal Fusion for 3D Object DetectionGuowen Zhang, Chenhang He, Liyi Chen, Lei ZhangAAAI 2026 · 被引用 2 次
- Photo3D: Advancing Photorealistic 3D Generation through Structure-Aligned Detail EnhancementXinyue Liang, Zhiyuan Ma, Lingchen Sun, Yanjun Guo 等CVPR 2026 · 被引用 1 次
它引用的顶会 Paper39
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh 等ICML 2021 · 被引用 47,906 次
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 被引用 35,902 次
- Segment AnythingAlexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao 等ICCV 2023 · 被引用 13,211 次
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
- Adding Conditional Control to Text-to-Image Diffusion ModelsLvmin Zhang, Anyi Rao, Maneesh AgrawalaICCV 2023 · 被引用 6,759 次
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