  We study the recent progress on dynamic view synthesis (DVS) from monocular
video. Though existing approaches have demonstrated impressive results, we show
a discrepancy between the practical capture process and the existing
experimental protocols, which effectively leaks in multi-view signals during
training. We define effective multi-view factors (EMFs) to quantify the amount
of multi-view signal present in the input capture sequence based on the
relative camera-scene motion. We introduce two new metrics: co-visibility
masked image metrics and correspondence accuracy, which overcome the issue in
existing protocols. We also propose a new iPhone dataset that includes more
diverse real-life deformation sequences. Using our proposed experimental
protocol, we show that the state-of-the-art approaches observe a 1-2 dB drop in
masked PSNR in the absence of multi-view cues and 4-5 dB drop when modeling
complex motion. Code and data can be found at https://hangg7.com/dycheck.
