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

Towards Efficient Neural Scene Graphs by Learning Consistency Fields