  Differently from 2D image datasets such as COCO, large-scale human datasets
with 3D ground-truth annotations are very difficult to obtain in the wild. In
this paper, we address this problem by augmenting existing 2D datasets with
high-quality 3D pose fits. Remarkably, the resulting annotations are sufficient
to train from scratch 3D pose regressor networks that outperform the current
state-of-the-art on in-the-wild benchmarks such as 3DPW. Additionally, training
on our augmented data is straightforward as it does not require to mix multiple
and incompatible 2D and 3D datasets or to use complicated network architectures
and training procedures. This simplified pipeline affords additional
improvements, including injecting extreme crop augmentations to better
reconstruct highly truncated people, and incorporating auxiliary inputs to
improve 3D pose estimation accuracy. It also reduces the dependency on 3D
datasets such as H36M that have restrictive licenses. We also use our method to
introduce new benchmarks for the study of real-world challenges such as
occlusions, truncations, and rare body poses. In order to obtain such high
quality 3D pseudo-annotations, inspired by progress in internal learning, we
introduce Exemplar Fine-Tuning (EFT). EFT combines the re-projection accuracy
of fitting methods like SMPLify with a 3D pose prior implicitly captured by a
pre-trained 3D pose regressor network. We show that EFT produces 3D annotations
that result in better downstream performance and are qualitatively preferable
in an extensive human-based assessment.
