  We present a method that learns a spatiotemporal neural irradiance field for
dynamic scenes from a single video. Our learned representation enables
free-viewpoint rendering of the input video. Our method builds upon recent
advances in implicit representations. Learning a spatiotemporal irradiance
field from a single video poses significant challenges because the video
contains only one observation of the scene at any point in time. The 3D
geometry of a scene can be legitimately represented in numerous ways since
varying geometry (motion) can be explained with varying appearance and vice
versa. We address this ambiguity by constraining the time-varying geometry of
our dynamic scene representation using the scene depth estimated from video
depth estimation methods, aggregating contents from individual frames into a
single global representation. We provide an extensive quantitative evaluation
and demonstrate compelling free-viewpoint rendering results.
