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

2026-07-13

AI Summary

This work addresses the challenge of balancing geometric consistency and camera controllability in large-baseline novel view synthesis from a single image. The authors propose a diffusion-based generative framework that integrates implicit geometric priors with sparse explicit metric depth cues. By leveraging a feedforward geometry-aware network to guide the generation process, the method achieves scale-consistent and structurally plausible view synthesis without requiring full 3D reconstruction. Experimental results demonstrate that the approach significantly outperforms existing methods under large viewpoint changes, exhibiting superior generalization capability and generation quality.

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 https://github.com/KlingAIResearch/MetaView.

Problem

• novel view synthesis
• monocular
• geometry consistency
• large viewpoint changes
• implicit geometry

Innovation

• implicit geometry priors
• monocular novel view synthesis
• scale-aware generation
• diffusion-based rendering
• metric depth