DyBluRF: Dynamic Deblurring Neural Radiance Fields for Blurry Monocular
  Video
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/.
658516d0bae0736365a28cf7
Minh-Quan Viet Bui
claimed_verified
2023-12-22T14:28:57.444Z
65859653c8b8bd66f7c5a651
/avatars/84825a8224350dae85784534e9c59424.svg
Quan Bui
user
quan5609
658516d0bae0736365a28cf8
Jongmin Park
658516d0bae0736365a28cf9
Jihyong Oh
admin_assigned
2023-12-22T13:01:37.324Z
6576b99d58ce19fa1e33eb1d
/avatars/b533e776aa3d95d722b46ef0cd381acd.svg
Jihyong Oh
user
ozbro
658516d0bae0736365a28cfa
Munchurl Kim
DyBluRF: Dynamic Deblurring Neural Radiance Fields for Blurry Monocular
  Video
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/.
A novel NeRF-based framework, DyBluRF, addresses motion blur in monocular video for precise video view synthesis by decomposing global and local motion.
Neural radiance fields NeRF motion blur dynamic deblurring interleave ray refinement motion decomposition latent sharp-rays prediction 3D scene reconstruction camera pose information