MetaView: Monocular Novel View Synthesis with Scale-Aware Implicit Geometry Priors

Current visual generation models are capable of producinghigh-quality content, yet they lack a coherent perception of the spatialstructure. Existing generative novel view synthesis methods typically in-troduce explicit geometry priors, which enforce spatial consistency butinherently restrict generalization in large view changes. In contrast, re-cent interactive generative methods favor implicit scene modeling, offer-ing greater flexibility at the cost of precise camera control and geome-try consistency. In this paper, we propose MetaView, a diffusion-basedmonocular novel view synthesis framework that enables rendering underlarge view changes from a single image. Our key insight is to combineimplicit geometry modeling with minimal yet essential explicit 3D cues:we incorporate implicit geometry priors from a feed-forward geometryperception network to regularize structure without imposing restrictivereconstruction pipelines, while leveraging metric depth to anchor the gen-eration to a metric scale. This design allows MetaView to achieve bothgeometry consistency and precise controllability. Extensive experimentsdemonstrate that, under challenging monocular large viewpoint changes,MetaView significantly outperforms existing methods and exhibits supe-rior generalization. Our code will be made publicly available.