  The task of three-dimensional (3D) human pose estimation from a single image
can be divided into two parts: (1) Two-dimensional (2D) human joint detection
from the image and (2) estimating a 3D pose from the 2D joints. Herein, we
focus on the second part, i.e., a 3D pose estimation from 2D joint locations.
The problem with existing methods is that they require either (1) a 3D pose
dataset or (2) 2D joint locations in consecutive frames taken from a video
sequence. We aim to solve these problems. For the first time, we propose a
method that learns a 3D human pose without any 3D datasets. Our method can
predict a 3D pose from 2D joint locations in a single image. Our system is
based on the generative adversarial networks, and the networks are trained in
an unsupervised manner. Our primary idea is that, if the network can predict a
3D human pose correctly, the 3D pose that is projected onto a 2D plane should
not collapse even if it is rotated perpendicularly. We evaluated the
performance of our method using Human3.6M and the MPII dataset and showed that
our network can predict a 3D pose well even if the 3D dataset is not available
during training.
