  With the explosive growth of available training data, single-image 3D human
modeling is ahead of a transition to a data-centric paradigm. A key to
successfully exploiting data scale is to design flexible models that can be
supervised from various heterogeneous data sources produced by different
researchers or vendors. To this end, we propose a simple yet powerful paradigm
for seamlessly unifying different human pose and shape-related tasks and
datasets. Our formulation is centered on the ability - both at training and
test time - to query any arbitrary point of the human volume, and obtain its
estimated location in 3D. We achieve this by learning a continuous neural field
of body point localizer functions, each of which is a differently parameterized
3D heatmap-based convolutional point localizer (detector). For generating
parametric output, we propose an efficient post-processing step for fitting
SMPL-family body models to nonparametric joint and vertex predictions. With
this approach, we can naturally exploit differently annotated data sources
including mesh, 2D/3D skeleton and dense pose, without having to convert
between them, and thereby train large-scale 3D human mesh and skeleton
estimation models that outperform the state-of-the-art on several public
benchmarks including 3DPW, EMDB and SSP-3D by a considerable margin.
