  Neural Radiance Fields (NeRF) achieves photo-realistic image rendering from
novel views, and the Neural Scene Graphs (NSG) \cite{ost2021neural} extends it
to dynamic scenes (video) with multiple objects. Nevertheless, computationally
heavy ray marching for every image frame becomes a huge burden. In this paper,
taking advantage of significant redundancy across adjacent frames in videos, we
propose a feature-reusing framework. From the first try of naively reusing the
NSG features, however, we learn that it is crucial to disentangle
object-intrinsic properties consistent across frames from transient ones. Our
proposed method, \textit{Consistency-Field-based NSG (CF-NSG)}, reformulates
neural radiance fields to additionally consider \textit{consistency fields}.
With disentangled representations, CF-NSG takes full advantage of the
feature-reusing scheme and performs an extended degree of scene manipulation in
a more controllable manner. We empirically verify that CF-NSG greatly improves
the inference efficiency by using 85\% less queries than NSG without notable
degradation in rendering quality. Code will be available at:
https://github.com/ldynx/CF-NSG
