MetaView：具有尺度感知隐式几何先验的单目新视角合成

摘要

当前视觉生成模型虽能生成高质量内容，但在空间结构连贯感知方面存在不足。现有的生成式新视角合成方法通常引入显式几何先验，虽能保证空间一致性，却在大视角变换场景下固有限制了泛化能力。相反，近期交互式生成方法倾向于隐式场景建模，在牺牲精确相机控制与几何一致性的前提下提供了更高灵活性。本文提出MetaView——一种基于扩散模型的单目新视角合成框架，能够从单张图像实现大视角变换下的渲染。我们的核心洞察在于将隐式几何建模与最小且必要的显式3D线索相结合：一方面通过前馈式几何感知网络引入隐式几何先验以约束结构，避免强加限制性重建流水线；另一方面利用度量深度将生成过程锚定于度量尺度。这种设计使MetaView同时实现几何一致性与精确可控性。大量实验表明，在极具挑战性的单目大视角变换场景中，MetaView显著优于现有方法，并展现出卓越的泛化能力。我们的代码已开源：https://github.com/KlingAIResearch/MetaView。

English

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.