  Recovering the 3D shape of a person from its 2D appearance is ill-posed due
to ambiguities. Nevertheless, with the help of convolutional neural networks
(CNN) and prior knowledge on the 3D human body, it is possible to overcome such
ambiguities to recover detailed 3D shapes of human bodies from single images.
Current solutions, however, fail to reconstruct all the details of a person
wearing loose clothes. This is because of either (a) huge memory requirement
that cannot be maintained even on modern GPUs or (b) the compact 3D
representation that cannot encode all the details. In this paper, we propose
the tetrahedral outer shell volumetric truncated signed distance function
(TetraTSDF) model for the human body, and its corresponding part connection
network (PCN) for 3D human body shape regression. Our proposed model is
compact, dense, accurate, and yet well suited for CNN-based regression task.
Our proposed PCN allows us to learn the distribution of the TSDF in the
tetrahedral volume from a single image in an end-to-end manner. Results show
that our proposed method allows to reconstruct detailed shapes of humans
wearing loose clothes from single RGB images.
