  Monocular 3D human pose and shape estimation is an ill-posed problem since
multiple 3D solutions can explain a 2D image of a subject. Recent approaches
predict a probability distribution over plausible 3D pose and shape parameters
conditioned on the image. We show that these approaches exhibit a trade-off
between three key properties: (i) accuracy - the likelihood of the ground-truth
3D solution under the predicted distribution, (ii) sample-input consistency -
the extent to which 3D samples from the predicted distribution match the
visible 2D image evidence, and (iii) sample diversity - the range of plausible
3D solutions modelled by the predicted distribution. Our method, HuManiFlow,
predicts simultaneously accurate, consistent and diverse distributions. We use
the human kinematic tree to factorise full body pose into ancestor-conditioned
per-body-part pose distributions in an autoregressive manner. Per-body-part
distributions are implemented using normalising flows that respect the manifold
structure of SO(3), the Lie group of per-body-part poses. We show that
ill-posed, but ubiquitous, 3D point estimate losses reduce sample diversity,
and employ only probabilistic training losses. Code is available at:
https://github.com/akashsengupta1997/HuManiFlow.
