title: High-Fidelity Novel View Synthesis via Splatting-Guided Diffusion
summary: 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_authors._id: 67b74fbdbb87b88059a9c5d3
paper_authors.name: Xiang Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-20T16:06:01.193Z
paper_authors._id: 657dc1576dc01435cd9029d8
paper_authors.avatarUrl: /avatars/3bba11ac7659fce61aeaedf40e2057a8.svg
paper_authors.fullname: Xiang Zhang
paper_authors.type: user
paper_authors.user: XiangZ
paper_authors._id: 67b74fbdbb87b88059a9c5d4
paper_authors.name: Yang Zhang
paper_authors._id: 67b74fbdbb87b88059a9c5d5
paper_authors.name: Lukas Mehl
paper_authors._id: 67b74fbdbb87b88059a9c5d6
paper_authors.name: Markus Gross
paper_authors._id: 67b74fbdbb87b88059a9c5d7
paper_authors.name: Christopher Schroers
paper_title: High-Fidelity Novel View Synthesis via Splatting-Guided Diffusion
paper_summary: 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_ai_summary: SplatDiff combines splatting and diffusion techniques to produce high-fidelity novel views with consistent geometry and texture from single images, outperforming existing methods in single-view and sparse-view synthesis.
paper_ai_keywords: splatting-based approaches diffusion-based methods video diffusion model aligned synthesis strategy texture bridge module adaptive feature fusion Novel View Synthesis geometry-consistent view synthesis texture hallucination zero-shot performance