Neural Radiance Fields (NeRFs) have made significant advances in rendering novel photorealistic views for both static and dynamic scenes. However, most prior works assume ideal conditions of artifact-free visual inputs i.e., images and videos. In real scenarios, artifacts such as object motion blur, camera motion blur, or lens defocus blur are ubiquitous. Some recent studies have explored novel view synthesis using blurred input frames by examining either camera motion blur, defocus blur, or both. However, these studies are limited to static scenes. In this work, we enable NeRFs to deal with object motion blur whose local nature stems from the interplay between object velocity and camera exposure time. Often, the object motion is unknown and time varying, and this adds to the complexity of scene reconstruction. Sports videos are a prime example of how rapid object motion can significantly degrade video quality for static cameras by introducing motion blur. We present an approach for realizing motion blur-free novel views of dynamic scenes from input videos with object motion blur captured from static cameras spanning multiple poses. We propose a NeRF-based analytical framework that elegantly correlates object three-dimensional (3D) motion across views as well as time to the observed blurry videos. Our proposed method DynaMoDe-NeRF (Dynamic Motion-aware Deblurring NeRF) is self-supervised and reconstructs the dynamic 3D scene, renders sharp novel views by blind deblurring, and recovers the underlying 3D motion and blur parameters. We provide comprehensive experimental analysis on synthetic and real data to validate our approach. To the best of our knowledge, this is the first work to address localized object motion blur in the NeRF domain.