  The SMPL body model is widely used for the estimation, synthesis, and
analysis of 3D human pose and shape. While popular, we show that SMPL has
several limitations and introduce STAR, which is quantitatively and
qualitatively superior to SMPL. First, SMPL has a huge number of parameters
resulting from its use of global blend shapes. These dense pose-corrective
offsets relate every vertex on the mesh to all the joints in the kinematic
tree, capturing spurious long-range correlations. To address this, we define
per-joint pose correctives and learn the subset of mesh vertices that are
influenced by each joint movement. This sparse formulation results in more
realistic deformations and significantly reduces the number of model parameters
to 20% of SMPL. When trained on the same data as SMPL, STAR generalizes better
despite having many fewer parameters. Second, SMPL factors pose-dependent
deformations from body shape while, in reality, people with different shapes
deform differently. Consequently, we learn shape-dependent pose-corrective
blend shapes that depend on both body pose and BMI. Third, we show that the
shape space of SMPL is not rich enough to capture the variation in the human
population. We address this by training STAR with an additional 10,000 scans of
male and female subjects, and show that this results in better model
generalization. STAR is compact, generalizes better to new bodies and is a
drop-in replacement for SMPL. STAR is publicly available for research purposes
at http://star.is.tue.mpg.de.
