  3D human mesh recovery from point clouds is essential for various tasks,
including AR/VR and human behavior understanding. Previous works in this field
either require high-quality 3D human scans or sequential point clouds, which
cannot be easily applied to low-quality 3D scans captured by consumer-level
depth sensors. In this paper, we make the first attempt to reconstruct reliable
3D human shapes from single-frame partial point clouds.To achieve this, we
propose an end-to-end learnable method, named VoteHMR. The core of VoteHMR is a
novel occlusion-aware voting network that can first reliably produce visible
joint-level features from the input partial point clouds, and then complete the
joint-level features through the kinematic tree of the human skeleton. Compared
with holistic features used by previous works, the joint-level features can not
only effectively encode the human geometry information but also be robust to
noisy inputs with self-occlusions and missing areas. By exploiting the rich
complementary clues from the joint-level features and global features from the
input point clouds, the proposed method encourages reliable and disentangled
parameter predictions for statistical 3D human models, such as SMPL. The
proposed method achieves state-of-the-art performances on two large-scale
datasets, namely SURREAL and DFAUST. Furthermore, VoteHMR also demonstrates
superior generalization ability on real-world datasets, such as Berkeley MHAD.
