title: DyBluRF: Dynamic Deblurring Neural Radiance Fields for Blurry Monocular
    Video
summary: Video view synthesis, allowing for the creation of visually appealing frames
  from arbitrary viewpoints and times, offers immersive viewing experiences.
  Neural radiance fields, particularly NeRF, initially developed for static
  scenes, have spurred the creation of various methods for video view synthesis.
  However, the challenge for video view synthesis arises from motion blur, a
  consequence of object or camera movement during exposure, which hinders the
  precise synthesis of sharp spatio-temporal views. In response, we propose a
  novel dynamic deblurring NeRF framework for blurry monocular video, called
  DyBluRF, consisting of an Interleave Ray Refinement (IRR) stage and a Motion
  Decomposition-based Deblurring (MDD) stage. Our DyBluRF is the first that
  addresses and handles the novel view synthesis for blurry monocular video. The
  IRR stage jointly reconstructs dynamic 3D scenes and refines the inaccurate
  camera pose information to combat imprecise pose information extracted from the
  given blurry frames. The MDD stage is a novel incremental latent sharp-rays
  prediction (ILSP) approach for the blurry monocular video frames by decomposing
  the latent sharp rays into global camera motion and local object motion
  components. Extensive experimental results demonstrate that our DyBluRF
  outperforms qualitatively and quantitatively the very recent state-of-the-art
  methods. Our project page including source codes and pretrained model are
  publicly available at https://kaist-viclab.github.io/dyblurf-site/.
paper_authors._id: 658516d0bae0736365a28cf7
paper_authors.name: Minh-Quan Viet Bui
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-22T14:28:57.444Z
paper_authors._id: 65859653c8b8bd66f7c5a651
paper_authors.avatarUrl: /avatars/84825a8224350dae85784534e9c59424.svg
paper_authors.fullname: Quan Bui
paper_authors.type: user
paper_authors.user: quan5609
paper_authors._id: 658516d0bae0736365a28cf8
paper_authors.name: Jongmin Park
paper_authors._id: 658516d0bae0736365a28cf9
paper_authors.name: Jihyong Oh
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-22T13:01:37.324Z
paper_authors._id: 6576b99d58ce19fa1e33eb1d
paper_authors.avatarUrl: /avatars/b533e776aa3d95d722b46ef0cd381acd.svg
paper_authors.fullname: Jihyong Oh
paper_authors.type: user
paper_authors.user: ozbro
paper_authors._id: 658516d0bae0736365a28cfa
paper_authors.name: Munchurl Kim
paper_title: DyBluRF: Dynamic Deblurring Neural Radiance Fields for Blurry Monocular
    Video
paper_summary: Video view synthesis, allowing for the creation of visually appealing frames
  from arbitrary viewpoints and times, offers immersive viewing experiences.
  Neural radiance fields, particularly NeRF, initially developed for static
  scenes, have spurred the creation of various methods for video view synthesis.
  However, the challenge for video view synthesis arises from motion blur, a
  consequence of object or camera movement during exposure, which hinders the
  precise synthesis of sharp spatio-temporal views. In response, we propose a
  novel dynamic deblurring NeRF framework for blurry monocular video, called
  DyBluRF, consisting of an Interleave Ray Refinement (IRR) stage and a Motion
  Decomposition-based Deblurring (MDD) stage. Our DyBluRF is the first that
  addresses and handles the novel view synthesis for blurry monocular video. The
  IRR stage jointly reconstructs dynamic 3D scenes and refines the inaccurate
  camera pose information to combat imprecise pose information extracted from the
  given blurry frames. The MDD stage is a novel incremental latent sharp-rays
  prediction (ILSP) approach for the blurry monocular video frames by decomposing
  the latent sharp rays into global camera motion and local object motion
  components. Extensive experimental results demonstrate that our DyBluRF
  outperforms qualitatively and quantitatively the very recent state-of-the-art
  methods. Our project page including source codes and pretrained model are
  publicly available at https://kaist-viclab.github.io/dyblurf-site/.
paper_ai_keywords: neural radiance fields NeRF video view synthesis motion blur dynamic deblurring DyBluRF Interleave Ray Refinement IRR Motion Decomposition-based Deblurring MDD dynamic 3D scenes camera pose information incremental latent sharp-rays prediction ILSP global camera motion local object motion