  3D human pose estimation is frequently seen as the task of estimating 3D
poses relative to the root body joint. Alternatively, we propose a 3D human
pose estimation method in camera coordinates, which allows effective
combination of 2D annotated data and 3D poses and a straightforward multi-view
generalization. To that end, we cast the problem as a view frustum space pose
estimation, where absolute depth prediction and joint relative depth
estimations are disentangled. Final 3D predictions are obtained in camera
coordinates by the inverse camera projection. Based on this, we also present a
consensus-based optimization algorithm for multi-view predictions from
uncalibrated images, which requires a single monocular training procedure.
Although our method is indirectly tied to the training camera intrinsics, it
still converges for cameras with different intrinsic parameters, resulting in
coherent estimations up to a scale factor. Our method improves the state of the
art on well known 3D human pose datasets, reducing the prediction error by 32%
in the most common benchmark. We also reported our results in absolute pose
position error, achieving 80~mm for monocular estimations and 51~mm for
multi-view, on average.
