  3D human segmentation has seen noticeable progress in re-cent years. It,
however, still remains a challenge to date. In this paper, weintroduce a deep
patch-based method for 3D human segmentation. Wefirst extract a local surface
patch for each vertex and then parameterizeit into a 2D grid (or image). We
then embed identified shape descriptorsinto the 2D grids which are further fed
into the powerful 2D Convolu-tional Neural Network for regressing corresponding
semantic labels (e.g.,head, torso). Experiments demonstrate that our method is
effective inhuman segmentation, and achieves state-of-the-art accuracy.
