MetaView: Monocular Novel View Synthesis with Scale-Aware Implicit Geometry Priors
MetaView is a diffusion-based monocular novel view synthesis framework that enables rendering under large view changes from a single image. It combines implicit geometry priors with metric depth to achieve geometry consistency and precise controllability, outperforming existing methods.
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[Submitted on 13 Jul 2026]
Title:MetaView: Monocular Novel View Synthesis with Scale-Aware Implicit Geometry Priors
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Abstract:Current visual generation models are capable of producing high-quality content, yet they lack a coherent perception of the spatial structure. Existing generative novel view synthesis methods typically introduce explicit geometry priors, which enforce spatial consistency but inherently restrict generalization in large view changes. In contrast, recent interactive generative methods favor implicit scene modeling, offering greater flexibility at the cost of precise camera control and geometry consistency. In this paper, we propose MetaView, a diffusion-based monocular novel view synthesis framework that enables rendering under large view changes from a single image. Our key insight is to combine implicit geometry modeling with minimal yet essential explicit 3D cues: we incorporate implicit geometry priors from a feed-forward geometry perception network to regularize structure without imposing restrictive reconstruction pipelines, while leveraging metric depth to anchor the generation to a metric scale. This design allows MetaView to achieve both geometry consistency and precise controllability. Extensive experiments demonstrate that, under challenging monocular large viewpoint changes, MetaView significantly outperforms existing methods and exhibits superior generalization. Our code is publicly available at this https URL.
Comments: accepted to ECCV 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.12000 [cs.CV]
(or arXiv:2607.12000v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.12000
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yufei Cai [view email] [v1] Mon, 13 Jul 2026 17:51:32 UTC (16,636 KB)
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