The following sections introduce our methods for scene-
motion-adjusted multi-view feature aggregation (Sec. 3.1),
and enforcing temporal consistency via cross-time rendering
in motion-adjusted ray space (Sec. 3.2). Our full system
combines a static model and a dynamic model to produce a
color at each pixel. Accurate scene factorization is achieved
via segmentation masks derived from a separately trained
motion segmentation module within a Bayesian learning
framework (Sec. 3.3).
3.1. Motion-adjusted feature aggregation

We synthesize new views by aggregating features ex-
tracted from temporally nearby source views. To render an
image at time i, we ﬁrst identify source views Ij within a
temporal radius r frames of i, j ∈ N (i) = [i − r, i + r]. For
each source view, we extract a 2D feature map Fi through a
DynIBaR-NeuralDynamicImage-BasedRendering