canonical to the posed space, and projecting them onto the
2D image space. However, to train these models, one needs
to unpose scans into the canonical pose with an accurately
ﬁtted body model; inaccurate poses cause artifacts. More-
over, unposing clothed scans using the “undressed” model’s
skinning weights alters shape details. For the same RGB
input, Zheng et al. [69,70] condition the implicit function on
a posed and voxelized SMPL mesh for robustness to pose
variation and reconstruct local details from the image pixels,
similar to PIFu [54]. However, these methods are sensitive
to global pose, due to their 3D convolutional encoder. Thus,
for training data with limited pose variation, they struggle
with out-of-distribution poses and in-the-wild images.
ICON