MetaView：基於尺度感知隱式幾何先驗的單目新視角合成

摘要

當前視覺生成模型雖能產出高品質內容，卻缺乏對空間結構的一致感知。現有生成式新視角合成方法通常引入明確的幾何先驗，以強制空間一致性，但本質上限制了大幅視角變化的泛化能力。反之，近期互動式生成方法偏好隱式場景建模，提供更大靈活性，卻犧牲了精確的相機控制與幾何一致性。本文提出MetaView，這是一個基於擴散的單目新視角合成框架，能從單張影像實現大幅視角變化的渲染。我們的關鍵洞察在於結合隱式幾何建模與最小但必要的明確3D線索：我們從前饋幾何感知網路中引入隱式幾何先驗，以規範結構而不強加嚴格的重建流程，同時利用度量深度將生成過程錨定於度量尺度。此設計使MetaView能同時達成幾何一致性與精確可控性。大量實驗證明，在具挑戰性的單目大幅視角變化情境下，MetaView顯著優於現有方法，並展現優越的泛化能力。我們的程式碼已公開於 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.