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 this https URL

提出一种用于模糊单目视频的动态去模糊 NeRF 框架（DyBluRF），通过互联的射线细化阶段（IRR）和基于运动分解的去模糊阶段（MDD），首次处理和处理了模糊单目视频的新视图合成。