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.

提出了一种基于 CNN 的方法，利用现有的 3D 姿势数据和 2D 姿势数据进行迁移学习，在真实场景中实现了最先进的性能，同时引入了一个人类身体姿势估计的新训练集，并提出了一个覆盖室内和室外场景的新基准。