  Despite the significant improvement in the performance of monocular pose
estimation approaches and their ability to generalize to unseen environments,
multi-view (MV) approaches are often lagging behind in terms of accuracy and
are specific to certain datasets. This is mainly due to the fact that (1)
contrary to real world single-view (SV) datasets, MV datasets are often
captured in controlled environments to collect precise 3D annotations, which do
not cover all real world challenges, and (2) the model parameters are learned
for specific camera setups. To alleviate these problems, we propose a two-stage
approach to detect and estimate 3D human poses, which separates SV pose
detection from MV 3D pose estimation. This separation enables us to utilize
each dataset for the right task, i.e. SV datasets for constructing robust pose
detection models and MV datasets for constructing precise MV 3D regression
models. In addition, our 3D regression approach only requires 3D pose data and
its projections to the views for building the model, hence removing the need
for collecting annotated data from the test setup. Our approach can therefore
be easily generalized to a new environment by simply projecting 3D poses into
2D during training according to the camera setup used at test time. As 2D poses
are collected at test time using a SV pose detector, which might generate
inaccurate detections, we model its characteristics and incorporate this
information during training. We demonstrate that incorporating the detector's
characteristics is important to build a robust 3D regression model and that the
resulting regression model generalizes well to new MV environments. Our
evaluation results show that our approach achieves competitive results on the
Human3.6M dataset and significantly improves results on a MV clinical dataset
that is the first MV dataset generated from live surgery recordings.
