DeblurNVS: Geometric Latent Diffusion for Novel View Synthesis from Sparse Motion-Blurred Images

2026-05-31

AI Summary

This work addresses the challenge of novel view synthesis from motion-blurred inputs, a setting where existing methods struggle due to inconsistent geometry and appearance, while conventional deblurring approaches rely on costly per-scene optimization and exhibit limited generalization. We propose DeblurNVS, the first framework capable of synthesizing sharp, multi-view-consistent novel views from sparse, motion-blurred images without per-scene optimization. Our method leverages a geometric latent diffusion model to recover coherent 3D structure and correspondences from blurred inputs, which are then integrated with either neural radiance fields or 3D Gaussian splatting to render high-fidelity views. Extensive experiments on both synthetic and real-world blurred scenes demonstrate that DeblurNVS significantly outperforms state-of-the-art alternatives, producing structurally stable and visually clear results while enabling efficient and generalizable sparse-view synthesis.

Abstract

Novel view synthesis (NVS) is a fundamental problem in computer vision and graphics. Recent advances in neural radiance fields (NeRF), 3D Gaussian Splatting (3DGS), and generative view synthesis have substantially improved its quality. Yet most methods still rely on clean observations, where image structures and cross-view geometric cues are well preserved. Motion blur breaks this assumption by corrupting local details and weakening multi-view correspondences. Such blur commonly arises from camera shake, scene motion, or finite exposure in practical capture. Blur-aware NVS methods address this degradation by modeling image formation, but their reliance on costly per-scene optimization limits efficient and generalizable sparse-view synthesis. To address this, we propose DeblurNVS, a novel framework for synthesizing high-fidelity novel views directly from sparse motion-blurred images, without requiring per-scene optimization. DeblurNVS restores the intermediate geometric representations needed for multi-view reasoning, enabling blurred inputs to recover reliable structure and correspondence cues. The restored representations are then combined with target camera information to synthesize the target-view representation and reconstruct a sharp RGB novel view. To enable the large-scale training, we construct a motion-blurred NVS dataset from DL3DV-10K using interpolation-based finite-exposure blur synthesis. Extensive experiments demonstrate that DeblurNVS outperforms existing baselines on synthetic motion-blur benchmarks and generalizes to real motion-blurred scenes, producing perceptually sharper and structurally more stable novel views while avoiding costly per-scene optimization. Project page: https://github.com/PKU-YuanGroup/DeblurNVS.

Problem

• Novel View Synthesis
• Motion Blur
• Sparse Views
• Geometric Correspondence
• Image Deblurring

Innovation

• motion-blurred novel view synthesis
• geometric latent diffusion
• per-scene optimization-free
• intermediate geometric representation
• sparse-view synthesis