We represent geometry using a signed distance ﬁeld
(SDF). Since existing fashion datasets are imbalanced and
contain mostly frontal views, learning correct 3D geometry
from such datasets is difﬁcult. Following [23], we exploit
a human shape prior in the form of canonical SMPL [38].
Speciﬁcally, for every query point x in canonical space, we
predict an SDF offset ∆d(x, z, p) from the base shape to
model details such as hair and clothing. The SDF value
d(x, z, p) is then calculated as

d = d(x, z, p) = dSMPL(x) + ∆d(x, z, p),

(1)

where dSMPL(x) is the signed distance to the canonical
SMPL surface. Unlike [23], to compute dSMPL(x) efﬁ-
ciently, we represent the SDF as a low-resolution voxel grid
(128 × 128 × 32), where the value of every grid point is the
(pre-computed) distance to the SMPL mesh. We then query
dSMPL(x) by trilinear interpolating the SDF voxel grid.
AG3D- Learning to Generate 3D Avatars from 2D Image Collections