  Registering point clouds of dressed humans to parametric human models is a
challenging task in computer vision. Traditional approaches often rely on
heavily engineered pipelines that require accurate manual initialization of
human poses and tedious post-processing. More recently, learning-based methods
are proposed in hope to automate this process. We observe that pose
initialization is key to accurate registration but existing methods often fail
to provide accurate pose initialization. One major obstacle is that, regressing
joint rotations from point clouds or images of humans is still very
challenging. To this end, we propose novel piecewise transformation fields
(PTF), a set of functions that learn 3D translation vectors to map any query
point in posed space to its correspond position in rest-pose space. We combine
PTF with multi-class occupancy networks, obtaining a novel learning-based
framework that learns to simultaneously predict shape and per-point
correspondences between the posed space and the canonical space for clothed
human. Our key insight is that the translation vector for each query point can
be effectively estimated using the point-aligned local features; consequently,
rigid per bone transformations and joint rotations can be obtained efficiently
via a least-square fitting given the estimated point correspondences,
circumventing the challenging task of directly regressing joint rotations from
neural networks. Furthermore, the proposed PTF facilitate canonicalized
occupancy estimation, which greatly improves generalization capability and
results in more accurate surface reconstruction with only half of the
parameters compared with the state-of-the-art. Both qualitative and
quantitative studies show that fitting parametric models with poses initialized
by our network results in much better registration quality, especially for
extreme poses.
