4. Implementation details
Data. We conduct numerical evaluations on the Nvidia
Dynamic Scene Dataset [75] and UCSD Dynamic Scenes
Dataset [37]. Each dataset consists of eight forward-facing
dynamic scenes recorded by synchronized multi-view cam-
eras. We follow prior work [35] to derive a monocular video
from each sequence, where each video contains 100∼250
frames. We removed frames that lack large regions of mov-
ing objects. We follow the protocol from prior work [35] that
uses held-out images per time instance for evaluation. We
also tested the methods on in-the-wild monocular videos,
which feature more challenging camera and object mo-
tions [20].
View selection. For the time-varying, dynamic model we
use a frame window radius of r = 3 for all experiments. For
the time-invariant model representing static scene content,
we use separate strategies for the dynamic scenes bench-
marks and for in-the-wild videos. For the benchmarks, where
DynIBaR-NeuralDynamicImage-BasedRendering