ing inference of clothed-body normal maps, (cid:98)N c (Sec. 3.1).

Table 2-B shows performance with (“ICON”) and without
(“ICONN†”) conditioning. With no conditioning, errors on
“CAPE” increase slightly. Qualitatively, guidance by body
normals heavily improves the inferred normals, especially
for occluded body regions; see Fig. 5. We also ablate the ef-
fect of the body-normal feature (Sec. 3.2), F b
n , by removing
it; this worsens results, see “ICON w/o F b

n ” in Tab. 2-B.

C. Local-feature based implicit reconstruction. To
evaluate the importance of our “local” features (Sec. 3.2),
FP, we replace them with “global” features produced by 2D

Figure 5. Normal prediction ((cid:98)N c) w/ and w/o SMPL prior (N b).

convolutional ﬁlters. These are applied on the image and the

clothed-body normal maps (“ICONenc(I,(cid:98)N c)” in Tab. 2-C),
or only on the normal maps (“ICONenc((cid:98)N c)” in Tab. 2-C).
ICON