  Human pose estimation from single images is a challenging problem that is
typically solved by supervised learning. Unfortunately, labeled training data
does not yet exist for many human activities since 3D annotation requires
dedicated motion capture systems. Therefore, we propose an unsupervised
approach that learns to predict a 3D human pose from a single image while only
being trained with 2D pose data, which can be crowd-sourced and is already
widely available. To this end, we estimate the 3D pose that is most likely over
random projections, with the likelihood estimated using normalizing flows on 2D
poses. While previous work requires strong priors on camera rotations in the
training data set, we learn the distribution of camera angles which
significantly improves the performance. Another part of our contribution is to
stabilize training with normalizing flows on high-dimensional 3D pose data by
first projecting the 2D poses to a linear subspace. We outperform the
state-of-the-art unsupervised human pose estimation methods on the benchmark
datasets Human3.6M and MPI-INF-3DHP in many metrics.
