  Recent work has shown the benefits of synthetic data for use in computer
vision, with applications ranging from autonomous driving to face landmark
detection and reconstruction. There are a number of benefits of using synthetic
data from privacy preservation and bias elimination to quality and feasibility
of annotation. Generating human-centered synthetic data is a particular
challenge in terms of realism and domain-gap, though recent work has shown that
effective machine learning models can be trained using synthetic face data
alone. We show that this can be extended to include the full body by building
on the pipeline of Wood et al. to generate synthetic images of humans in their
entirety, with ground-truth annotations for computer vision applications.
  In this report we describe how we construct a parametric model of the face
and body, including articulated hands; our rendering pipeline to generate
realistic images of humans based on this body model; an approach for training
DNNs to regress a dense set of landmarks covering the entire body; and a method
for fitting our body model to dense landmarks predicted from multiple views.
