Most prior work on novel view synthesis for dynamic
scenes requires multiple synchronized input videos [1, 3,
6, 27, 33, 63, 69, 76, 82], limiting their real-world applica-
bility. Some methods [8, 13, 22, 51, 71] use domain knowl-
edge such as template models to achieve high-quality re-
sults, but are restricted to speciﬁc categories [41, 56]. More
recently, many works propose to synthesize novel views
of dynamic scenes from a single camera. Yoon et al. [75]
render novel views through explicit warping using depth
maps obtained via single-view depth and multi-view stereo.
However, this method fails to model complex scene geom-
etry and to ﬁll in realistic and consistent content at disoc-
clusions. With advances in neural rendering, NeRF-based
dynamic view synthesis methods have shown state-of-the-
art results [16, 35, 53, 66, 74]. Some approaches, such as
Nerﬁes [49] and HyperNeRF [50], represent scenes using a
deformation ﬁeld mapping each local observation to a canoni-
DynIBaR-NeuralDynamicImage-BasedRendering