  We propose a CNN-based approach for 3D human body pose estimation from single
RGB images that addresses the issue of limited generalizability of models
trained solely on the starkly limited publicly available 3D pose data. Using
only the existing 3D pose data and 2D pose data, we show state-of-the-art
performance on established benchmarks through transfer of learned features,
while also generalizing to in-the-wild scenes. We further introduce a new
training set for human body pose estimation from monocular images of real
humans that has the ground truth captured with a multi-camera marker-less
motion capture system. It complements existing corpora with greater diversity
in pose, human appearance, clothing, occlusion, and viewpoints, and enables an
increased scope of augmentation. We also contribute a new benchmark that covers
outdoor and indoor scenes, and demonstrate that our 3D pose dataset shows
better in-the-wild performance than existing annotated data, which is further
improved in conjunction with transfer learning from 2D pose data. All in all,
we argue that the use of transfer learning of representations in tandem with
algorithmic and data contributions is crucial for general 3D body pose
estimation.
