5. Evaluation
5.1. Baselines and error metrics

We compare our approach to state-of-the-art monocu-
lar view synthesis methods. Speciﬁcally, we compare to
three recent canonical space–based methods, Nerﬁes [49],
and HyperNeRF [50], and to two scene ﬂow–based meth-
ods, NSFF [35] and Dynamic View Synthesis (DVS) from
Gao et al. [19]. For fair comparisons, we use the same depth,
optical ﬂow and motion segmentation masks used for our
approach as inputs to other methods.

Following prior work [35], we report the rendering qual-
ity of each method with three standard error metrics: peak
signal-to-noise ratio (PSNR), structural similarity (SSIM),
and perceptual similarity via LPIPS [77], and calculate errors
both over the entire scene (Full) and restricted to moving
regions (Dynamic Only).
5.2. Quantitative evaluation
DynIBaR-NeuralDynamicImage-BasedRendering