ˆWj→i(r)ρ(Ci(r), ˆCj→i(r)).

(3)

(cid:88)

(cid:88)

r

j∈N (i)

Lpho =

We use a generalized Charbonnier loss [10] for the RGB
loss ρ. ˆWj→i(r) is a motion disocclusion weight computed
by the difference of accumulated alpha weights between
time i and j to address the motion disocclusion ambiguity
described by NSFF [35] (see supplement for more details).
Note that when j = i, there is no scene motion–induced
displacement, meaning ˆCj→i = ˆCi and no disocclusion
weights are involved ( ˆWj→i = 1). We show a comparison
between our method with and without enforcing temporal
consistency in the ﬁrst column of Fig. 4.
3.3. Combining static and dynamic models
DynIBaR-NeuralDynamicImage-BasedRendering