3

Figure 3. ICON’s architecture contains two main modules for: (1) body-guided normal prediction, and (2) local-feature based implicit 3D
reconstruction. The dotted line with an arrow is a 2D or 3D query function. The two GN networks (purple/orange) have different parameters.
ferentiable renderer, denoted as DR, to render M from two
opposite views, obtaining “front” (i.e., observable side) and
“back” (i.e., occluded side) SMPL-body normal maps N b =
back}. Given N b and the original color image I,
{N b
front, (cid:98)N c
back} predict clothed-
our normal networks GN = {GN
back}:

front,N b
body normal maps, denoted as (cid:98)N c = {(cid:98)N c
front,GN
GN(N b,I) → (cid:98)N c.

(1)
(2)
We train the normal networks, GN, with the following loss:

DR(M) → N b,

LN = Lpixel + λVGGLVGG,

v − (cid:98)N c
ICON