  Neural Radiance Fields (NeRF), initially developed for static scenes, have
inspired many video novel view synthesis techniques. 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 a Base Ray
Initialization (BRI) stage and a Motion Decomposition-based Deblurring (MDD)
stage. Our DyBluRF is the first that handles the novel view synthesis for
blurry monocular video with a novel two-stage framework. In the BRI stage, we
coarsely reconstruct dynamic 3D scenes and jointly initialize the base ray,
which is further used to predict latent sharp rays, using the inaccurate camera
pose information from the given blurry frames. In the MDD stage, we introduce 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. We further propose two loss
functions for effective geometry regularization and decomposition of static and
dynamic scene components without any mask supervision. Experiments show that
DyBluRF outperforms qualitatively and quantitatively the SOTA methods.
