(or ˆCfull

i

Figure 3. Temporal consistency via cross-time rendering. To en-
force temporal consistency in a dynamic reconstruction, we render
each frame Ii using the scene model from a nearby time j, which
we call cross-time rendering. A ray r from image i is instead ren-
dered using a curved ray ri→j, i.e., r warped to time j. That is,
having computed the motion-adjusted point xi→j = x+∆x,i(j) at
nearby time j from every sampled location along r, we query xi→j
and time j via the MLP to predict its motion trajectory Γxi→j ,j,
along which we aggregate image features fk extracted from source
views within time k ∈ N (j). The aggregated features along ri→j
are fed to the ray transformer with time embedding γ(j) to produce
per-sample color and density (cj, σj) at time j. A pixel color ˆCj→i
is computed by volume rendering (cj, σj), and then compared to
the ground truth color Ci to form a reconstruction loss Lpho.
DynIBaR-NeuralDynamicImage-BasedRendering