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Video view synthesis, allowing for the creation of visually appealing framesfrom arbitrary viewpoints and times, offers immersive viewing experiences.Neural radiance fields, particularly NeRF, initially developed for staticscenes, have spurred the creation of various methods for video view synthesis.However, the challenge for video view synthesis arises from motion blur, aconsequence of object or camera movement during exposure, which hinders theprecise synthesis of sharp spatio-temporal views. In response, we propose anovel dynamic deblurring NeRF framework for blurry monocular video, calledDyBluRF, consisting of an Interleave Ray Refinement (IRR) stage and a MotionDecomposition-based Deblurring (MDD) stage. Our DyBluRF is the first thataddresses and handles the novel view synthesis for blurry monocular video. TheIRR stage jointly reconstructs dynamic 3D scenes and refines the inaccuratecamera pose information to combat imprecise pose information extracted from thegiven blurry frames. The MDD stage is a novel incremental latent sharp-raysprediction (ILSP) approach for the blurry monocular video frames by decomposingthe latent sharp rays into global camera motion and local object motioncomponents. Extensive experimental results demonstrate that our DyBluRFoutperforms qualitatively and quantitatively the very recent state-of-the-artmethods. Our project page including source codes and pretrained model arepublicly available at https://kaist-viclab.github.io/dyblurf-site/.

DyBluRF: Dynamic Deblurring Neural Radiance Fields for Blurry Monocular Video