  Our ability to train end-to-end systems for 3D human pose estimation from
single images is currently constrained by the limited availability of 3D
annotations for natural images. Most datasets are captured using Motion Capture
(MoCap) systems in a studio setting and it is difficult to reach the
variability of 2D human pose datasets, like MPII or LSP. To alleviate the need
for accurate 3D ground truth, we propose to use a weaker supervision signal
provided by the ordinal depths of human joints. This information can be
acquired by human annotators for a wide range of images and poses. We showcase
the effectiveness and flexibility of training Convolutional Networks (ConvNets)
with these ordinal relations in different settings, always achieving
competitive performance with ConvNets trained with accurate 3D joint
coordinates. Additionally, to demonstrate the potential of the approach, we
augment the popular LSP and MPII datasets with ordinal depth annotations. This
extension allows us to present quantitative and qualitative evaluation in
non-studio conditions. Simultaneously, these ordinal annotations can be easily
incorporated in the training procedure of typical ConvNets for 3D human pose.
Through this inclusion we achieve new state-of-the-art performance for the
relevant benchmarks and validate the effectiveness of ordinal depth supervision
for 3D human pose.
