NeoMap: Training-free Novel-View Synthesis from Single Images and Videos

• URL: http://arxiv.org/abs/2607.01962v1
• Date: Thu, 02 Jul 2026 09:56:50 GMT
• Title: NeoMap: Training-free Novel-View Synthesis from Single Images and Videos
• Abstract summary: We study the challenging problem of novel view video synthesis from single images or monocular videos.<n>Existing methods, which operate under the assumption that pre-trained video models lack native novel view synthesis capability, often suffer from artifacts and compromised global scene consistency.<n>We introduce NeoMap, a novel training-free framework designed to locate high-fidelity, view-consistent novel view solutions from general pre-trained video models.
• Score: 17.92443113042059
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the challenging problem of novel view video synthesis from single images or monocular videos. Existing methods, which operate under the assumption that pre-trained video models lack native novel view synthesis capability and enforce view alignment via camera conditioning, task-specific fine-tuning, or stepwise hard denoising guidance, often suffer from artifacts and compromised global scene consistency. In this paper, we introduce NeoMap, a novel training-free framework designed to locate high-fidelity, view-consistent novel view solutions from general pre-trained video models. The key to our approach is the core insight that promising novel view solutions are inherently encoded within the natural video data manifold learned by pre-trained models, and the core challenge is simply to locate this optimal solution. We solve this via our core mechanism: convergent manifold alternating projection iterations that optimize the initial noise. Extensive experiments demonstrate that NeoMap significantly outperforms all existing methods across 3 standard novel view synthesis benchmarks, including the challenging Tanks-and-Temples, LLFF and DAVIS datasets, achieving state-of-the-art generation fidelity and top-tier view consistency.

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