  Recent advancements in dynamic neural radiance field methods have yielded
remarkable outcomes. However, these approaches rely on the assumption of sharp
input images. When faced with motion blur, existing dynamic NeRF methods often
struggle to generate high-quality novel views. In this paper, we propose
DyBluRF, a dynamic radiance field approach that synthesizes sharp novel views
from a monocular video affected by motion blur. To account for motion blur in
input images, we simultaneously capture the camera trajectory and object
Discrete Cosine Transform (DCT) trajectories within the scene. Additionally, we
employ a global cross-time rendering approach to ensure consistent temporal
coherence across the entire scene. We curate a dataset comprising diverse
dynamic scenes that are specifically tailored for our task. Experimental
results on our dataset demonstrate that our method outperforms existing
approaches in generating sharp novel views from motion-blurred inputs while
maintaining spatial-temporal consistency of the scene.
