One-Shot Refiner: Boosting Feed-forward Novel View Synthesis via One-Step Diffusion

We present a novel framework for high-fidelity novel view synthesis (NVS) from sparse images, addressing key limitations in recent feed-forward 3D Gaussian Splatting (3DGS) methods built on Vision Transformer (ViT) backbones. While ViT-based pipelines offer strong geometric priors, they are often constrained by low-resolution inputs due to computational costs. Moreover, existing generative enhancement methods tend to be 3D-agnostic, resulting in inconsistent structures across views, especially in unseen regions. To overcome these challenges, we design a Dual-Domain Detail Perception Module, which enables handling high-resolution images without being limited by the ViT backbone, and endows Gaussians with additional features to store high-frequency details. We develop a feature-guided diffusion network, which can preserve high-frequency details during the restoration process. We introduce a unified training strategy that enables joint optimization of the ViT-based geometric backbone and the diffusion-based refinement module. Experiments demonstrate that our method can maintain superior generation quality across multiple datasets.

Authors

• Yitong Dong
• Qi Zhang School of Computer Science, Fudan University, Shanghai, China Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
• Minchao Jiang Hangzhou VIVO Information Technology Co., Ltd, Xidian University
• Zhiqiang Wu East China Normal University
• Qingnan Fan vivo Mobile Communication Co., Ltd.
• Ying Feng Tencent AI Lab
• Huaqi Zhang Hangzhou VIVO Information Technology Co., Ltd
• Hujun Bao Zhejiang University
• Guofeng Zhang State Key Lab of CAD&CG, Zhejiang University

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