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.

提出 DyBluRF 方法，通过在受到运动模糊的单目视频中捕捉相机轨迹和场景内物体的离散余弦变换 (DCT) 轨迹，以及采用全局跨时间渲染方法来实现生成高质量、清晰的新视角，并保持场景的时空一致性。在经过特定定制的多样化动态场景数据集上进行的实验结果表明，该方法在从运动模糊输入生成清晰的新视角并保持场景的时空一致性方面优于现有方法。