  Human Body Dimensions Estimation (HBDE) is a task that an intelligent agent
can perform to attempt to determine human body information from images (2D) or
point clouds or meshes (3D). More specifically, if we define the HBDE problem
as inferring human body measurements from images, then HBDE is a difficult,
inverse, multi-task regression problem that can be tackled with machine
learning techniques, particularly convolutional neural networks (CNN). Despite
the community's tremendous effort to advance human shape analysis, there is a
lack of systematic experiments to assess CNNs estimation of human body
dimensions from images. Our contribution lies in assessing a CNN estimation
performance in a series of controlled experiments. To that end, we augment our
recently published neural anthropometer dataset by rendering images with
different camera distance. We evaluate the network inference absolute and
relative mean error between the estimated and actual HBDs. We train and
evaluate the CNN in four scenarios: (1) training with subjects of a specific
gender, (2) in a specific pose, (3) sparse camera distance and (4) dense camera
distance. Not only our experiments demonstrate that the network can perform the
task successfully, but also reveal a number of relevant facts that contribute
to better understand the task of HBDE.
