Most current approaches to HPS estimation learn to
regress a parametric 3D body model like SMPL [37] from
images using 2D joint locations as training data. Such joint
locations are easy for human annotators to label in images.
Supervising the training with joints, however, is not suffi-
cient to learn shape since an infinite number of body shapes
can share the same joints. For example, consider someone
who puts on weight. Their body shape changes but their
joints stay the same. Several recent methods employ addi-
tional 2D cues, such as the silhouette, to provide additional
shape cues [51,52]. Silhouettes, however, are influenced by
clothing and do not provide explicit 3D supervision. Syn-
thetic approaches [35], on the other hand, drape SMPL 3D
bodies in virtual clothing and render them in images. While
this provides ground-truth 3D shape, realistic synthesis of
clothed humans is challenging, resulting in a domain gap.
Accurate 3D Body Shape Regression using Metric and Semantic Attributes