c defined in canonical space
c(x)},

In practice, we solve for wi

c(Rix + ti)

.

wi

o(x) =

c (Rkx + tk)

We pack the set of {wi

Solving for a single set of weight volumes {wi

(5)
c(x)} in
o(x)} in observa-
canonical space, instead of N sets of {wi
tion space (corresponding to N input images), can lead to
better generalization as it avoids over-fitting [10, 69].
c(x)} into a single volume Wc(x)
with K channels. Rather than encode Wc with an MLP,
we choose an explicit volume representation for two rea-
sons: (1) Eq. 5 shows that K MLP evaluations would be
needed to compute each wi
o(x), infeasible for optimization
(K = 24 in our work); (2) an explicit volume with lim-
ited resolution resampled via trilinear interpolation provides
smoothness that can help regularize the optimization later.
In practice, during optimization, rather than directly solve
for volume Wc, we solve for parameters θskel of a CNN that
generates the volume from a random (constant) latent code
z:
HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video