单张照片转任意视角，AI终于不穿帮了
现在的AI能从一张照片生成新视角，但要么视角一偏大就变形，要么只能小范围转。MetaView把两种思路捏在一起：用深度信息定好空间尺度（比如知道椅子离墙多远），再用隐式几何模型补全看不见的部分，不硬套3D模板。结果就是，你给它一张正面照，它能转到侧面甚至背面，物体结构不散、比例不乱。它不是你明天就能用的产品，但做3D资产、虚拟拍摄的人可以盯着这个方向。
📄 原文摘要(英文)
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.