Training data. To compare models fairly, we factor out
differences in training data as explained in Sec. 4.1. Follow-
ing previous work [54, 55], we retrain all baselines on the
same 450 Renderpeople scans (subset of AGORA). Meth-
ods that require the 3D body prior (i.e., PaMIR, ICON) use
the SMPL-X meshes provided by AGORA. ICON’s GN and
IF modules are trained on the same data.

Testing data. We evaluate primarily on CAPE [41],
which no method uses for training, to test their genereliz-
ability. Speciﬁcally, we divide the CAPE dataset into the
“CAPE-FP” and “CAPE-NFP” sets that have “fashion” and
“non-fashion” poses, respectively, to better analyze the gen-
eralization to complex body poses; for details on data split-
ting please see Appx To evaluate performance without a
domain gap between train/test data, we also test all models
on “AGORA-50” [54, 55], which contains 50 samples from
AGORA that are different from the 450 used for training.
ICON