title: DiET-GS: Diffusion Prior and Event Stream-Assisted Motion Deblurring 3D
    Gaussian Splatting
summary: Reconstructing sharp 3D representations from blurry multi-view images are
  long-standing problem in computer vision. Recent works attempt to enhance
  high-quality novel view synthesis from the motion blur by leveraging
  event-based cameras, benefiting from high dynamic range and microsecond
  temporal resolution. However, they often reach sub-optimal visual quality in
  either restoring inaccurate color or losing fine-grained details. In this
  paper, we present DiET-GS, a diffusion prior and event stream-assisted motion
  deblurring 3DGS. Our framework effectively leverages both blur-free event
  streams and diffusion prior in a two-stage training strategy. Specifically, we
  introduce the novel framework to constraint 3DGS with event double integral,
  achieving both accurate color and well-defined details. Additionally, we
  propose a simple technique to leverage diffusion prior to further enhance the
  edge details. Qualitative and quantitative results on both synthetic and
  real-world data demonstrate that our DiET-GS is capable of producing
  significantly better quality of novel views compared to the existing baselines.
  Our project page is https://diet-gs.github.io
paper_authors._id: 67ece4b12511c2aab09b84ca
paper_authors.name: Seungjun Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-02T11:16:54.586Z
paper_authors._id: 662f5b1e5aeedf55d209741a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/662f5b1e5aeedf55d209741a/YdsbnRJPj_6PRfWbCNWfX.jpeg
paper_authors.fullname: Seungjun Lee
paper_authors.type: user
paper_authors.user: onandon
paper_authors._id: 67ece4b12511c2aab09b84cb
paper_authors.name: Gim Hee Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-02T12:29:18.073Z
paper_authors._id: 64bc7568b7375f6b84503ce2
paper_authors.avatarUrl: /avatars/d970c9990d9d84ac51ff225c1b26e304.svg
paper_authors.fullname: Gim Hee Lee
paper_authors.type: user
paper_authors.user: gimhee-lee
paper_title: DiET-GS: Diffusion Prior and Event Stream-Assisted Motion Deblurring 3D
    Gaussian Splatting
paper_summary: Reconstructing sharp 3D representations from blurry multi-view images are
  long-standing problem in computer vision. Recent works attempt to enhance
  high-quality novel view synthesis from the motion blur by leveraging
  event-based cameras, benefiting from high dynamic range and microsecond
  temporal resolution. However, they often reach sub-optimal visual quality in
  either restoring inaccurate color or losing fine-grained details. In this
  paper, we present DiET-GS, a diffusion prior and event stream-assisted motion
  deblurring 3DGS. Our framework effectively leverages both blur-free event
  streams and diffusion prior in a two-stage training strategy. Specifically, we
  introduce the novel framework to constraint 3DGS with event double integral,
  achieving both accurate color and well-defined details. Additionally, we
  propose a simple technique to leverage diffusion prior to further enhance the
  edge details. Qualitative and quantitative results on both synthetic and
  real-world data demonstrate that our DiET-GS is capable of producing
  significantly better quality of novel views compared to the existing baselines.
  Our project page is https://diet-gs.github.io
paper_ai_summary: DiET-GS, a diffusion prior and event stream-assisted framework, improves 3D representation reconstruction from motion-blurred images by enhancing color accuracy and detail preservation.
paper_ai_keywords: diffusion prior event stream motion deblurring 3DGS event double integral edge details