NEOMAP: Novel-View Synthesis via Noise Initialization by Manifold Alternating Projection

We study the challenging problem of novel view video syn-thesis from single images or monocular videos. Existing methods, whichoperate under the assumption that pre-trained video models lack nativenovel view synthesis capability and enforce view alignment via cameraconditioning, task-specific fine-tuning, or stepwise hard denoising guid-ance, often suffer from artifacts and compromised global scene consis-tency. In this paper, we introduce NeoMap, a novel training-free frame-work designed to locate high-fidelity, view-consistent novel view solutionsfrom general pre-trained video models. The key to our approach is thecore insight that promising novel view solutions are inherently encodedwithin the natural video data manifold learned by pre-trained models,and the core challenge is simply to locate this optimal solution. We solvethis via our core mechanism: convergent manifold alternating projectioniterations that optimize the initial noise. Extensive experiments demon-strate that NeoMap significantly outperforms all existing methods across3 standard novel view synthesis benchmarks, including the challengingTanks-and-Temples, LLFF and DAVIS datasets, achieving state-of-the-art generation fidelity and top-tier view consistency. Our code and dataare available at https://github.com/vLAR-group/NeoMap