  The ability to perceive 3D human bodies from a single image has a multitude
of applications ranging from entertainment and robotics to neuroscience and
healthcare. A fundamental challenge in human mesh recovery is in collecting the
ground truth 3D mesh targets required for training, which requires burdensome
motion capturing systems and is often limited to indoor laboratories. As a
result, while progress is made on benchmark datasets collected in these
restrictive settings, models fail to generalize to real-world "in-the-wild"
scenarios due to distribution shifts. We propose Domain Adaptive 3D Pose
Augmentation (DAPA), a data augmentation method that enhances the model's
generalization ability in in-the-wild scenarios. DAPA combines the strength of
methods based on synthetic datasets by getting direct supervision from the
synthesized meshes, and domain adaptation methods by using ground truth 2D
keypoints from the target dataset. We show quantitatively that finetuning with
DAPA effectively improves results on benchmarks 3DPW and AGORA. We further
demonstrate the utility of DAPA on a challenging dataset curated from videos of
real-world parent-child interaction.
