  This paper presents a novel self-supervised approach to reconstruct human
shape and pose from noisy point cloud data. Relying on large amount of dataset
with ground-truth annotations, recent learning-based approaches predict
correspondences for every vertice on the point cloud; Chamfer distance is
usually used to minimize the distance between a deformed template model and the
input point cloud. However, Chamfer distance is quite sensitive to noise and
outliers, thus could be unreliable to assign correspondences. To address these
issues, we model the probability distribution of the input point cloud as
generated from a parametric human model under a Gaussian Mixture Model. Instead
of explicitly aligning correspondences, we treat the process of correspondence
search as an implicit probabilistic association by updating the posterior
probability of the template model given the input. A novel self-supervised loss
is further derived which penalizes the discrepancy between the deformed
template and the input point cloud conditioned on the posterior probability.
Our approach is very flexible, which works with both complete point cloud and
incomplete ones including even a single depth image as input. Compared to
previous self-supervised methods, our method shows the capability to deal with
substantial noise and outliers. Extensive experiments conducted on various
public synthetic datasets as well as a very noisy real dataset (i.e. CMU
Panoptic) demonstrate the superior performance of our approach over the
state-of-the-art methods.
