High-Fidelity Novel View Synthesis via Splatting-Guided Diffusion

Xiang Zhang, Yang Zhang, Lukas Mehl, Markus Gross, Christopher Schroers

High-Fidelity Novel View Synthesis via Splatting-Guided Diffusion: 4 upvotes on Hugging Face Daily Papers, #26 of 31 papers on 2025-02-20. Day-by-day upvote history.

Despite recent advances in Novel View Synthesis (NVS), generating high-fidelity views from single or sparse observations remains a significant challenge. Existing splatting-based approaches often produce distorted geometry due to splatting errors. While diffusion-based methods leverage rich 3D priors to achieve improved geometry, they often suffer from texture hallucination. In this paper, we introduce SplatDiff, a pixel-splatting-guided video diffusion model designed to synthesize high-fidelity novel views from a single image. Specifically, we propose an aligned synthesis strategy for precise control of target viewpoints and geometry-consistent view synthesis. To mitigate texture hallucination, we design a texture bridge module that enables high-fidelity texture generation through adaptive feature fusion. In this manner, SplatDiff leverages the strengths of splatting and diffusion to generate novel views with consistent geometry and high-fidelity details. Extensive experiments verify the state-of-the-art performance of SplatDiff in single-view NVS. Additionally, without extra training, SplatDiff shows remarkable zero-shot performance across diverse tasks, including sparse-view NVS and stereo video conversion.

Paper page on Hugging Face · arXiv

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