  The task of reconstructing detailed 3D human body models from images is
interesting but challenging in computer vision due to the high freedom of human
bodies. In order to tackle the problem, we propose a coarse-to-fine method to
reconstruct a detailed 3D human body from multi-view images combining voxel
super-resolution based on learning the implicit representation. Firstly, the
coarse 3D models are estimated by learning an implicit representation based on
multi-scale features which are extracted by multi-stage hourglass networks from
the multi-view images. Then, taking the low resolution voxel grids which are
generated by the coarse 3D models as input, the voxel super-resolution based on
an implicit representation is learned through a multi-stage 3D convolutional
neural network. Finally, the refined detailed 3D human body models can be
produced by the voxel super-resolution which can preserve the details and
reduce the false reconstruction of the coarse 3D models. Benefiting from the
implicit representation, the training process in our method is memory efficient
and the detailed 3D human body produced by our method from multi-view images is
the continuous decision boundary with high-resolution geometry. In addition,
the coarse-to-fine method based on voxel super-resolution can remove false
reconstructions and preserve the appearance details in the final
reconstruction, simultaneously. In the experiments, our method quantitatively
and qualitatively achieves the competitive 3D human body reconstructions from
images with various poses and shapes on both the real and synthetic datasets.
