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arXiv · 2609.34579

GenNVS: Geometry-enhanced Novel View Synthesis via Disentangled 3D Prior

Yajiao Xiong·Youyu Luan·Xiaoyu Zhou·Yongtao Wang
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Abstract

Single-image novel view synthesis remains challenging because the underlying 3D geometry is highly ambiguous. Recent diffusion-based approaches produce plausible results, but they often struggle to preserve the geometric structure and spatial coherence of foreground objects. We present GenNVS, a framework for geometry-enhanced novel view synthesis via a disentangled 3D prior. Specifically, GenNVS models foreground objects and the background with 3D Gaussian Splatting and aligns them through a coarse-to-fine geometric optimization process to form a unified 3D scene. This scene conditions a video diffusion model through the proposed Dual-Stream Masking mechanism, which guides synthesis by jointly exploiting rendered validity masks and geometry-aware warping. Experimental results show that GenNVS performs favorably against recent methods in both visual quality and geometric accuracy, while naturally supporting flexible scene editing.

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Yajiao Xiong, Youyu Luan, Xiaoyu Zhou, Yongtao Wang. 2026-09-28. GenNVS: Geometry-enhanced Novel View Synthesis via Disentangled 3D Prior. https://arxiv.org/abs/2609.34579

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