GeoNVS: Geometry Grounded Video Diffusion for Novel View Synthesis

• URL: http://arxiv.org/abs/2603.14965v1
• Date: Mon, 16 Mar 2026 08:23:00 GMT
• Title: GeoNVS: Geometry Grounded Video Diffusion for Novel View Synthesis
• Abstract summary: GeoNVS is a novel-view synthesizer that enhances both geometric fidelity and camera controllability through explicit 3D geometric guidance.<n>GS-Adapter lifts input-view diffusion features into 3D Gaussian representations, and adaptively fuses them with diffusion features to correct geometrically inconsistent representations.<n>Experiments across 9 scenes and 18 settings demonstrate state-of-the-art performance, achieving 11.3% and 14.9% improvements over SEVA and CameraCtrl.
• Score: 73.97869945427645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Novel view synthesis requires strong 3D geometric consistency and the ability to generate visually coherent images across diverse viewpoints. While recent camera-controlled video diffusion models show promising results, they often suffer from geometric distortions and limited camera controllability. To overcome these challenges, we introduce GeoNVS, a geometry-grounded novel-view synthesizer that enhances both geometric fidelity and camera controllability through explicit 3D geometric guidance. Our key innovation is the Gaussian Splat Feature Adapter (GS-Adapter), which lifts input-view diffusion features into 3D Gaussian representations, renders geometry-constrained novel-view features, and adaptively fuses them with diffusion features to correct geometrically inconsistent representations. Unlike prior methods that inject geometry at the input level, GS-Adapter operates in feature space, avoiding view-dependent color noise that degrades structural consistency. Its plug-and-play design enables zero-shot compatibility with diverse feed-forward geometry models without additional training, and can be adapted to other video diffusion backbones. Experiments across 9 scenes and 18 settings demonstrate state-of-the-art performance, achieving 11.3% and 14.9% improvements over SEVA and CameraCtrl, with up to 2x reduction in translation error and 7x in Chamfer Distance.

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