  The ultimate goal for an inference model is to be robust and functional in
real life applications. However, training vs. test data domain gaps often
negatively affect model performance. This issue is especially critical for the
monocular 3D human pose estimation problem, in which 3D human data is often
collected in a controlled lab setting. In this paper, we focus on alleviating
the negative effect of domain shift in both appearance and pose space for 3D
human pose estimation by presenting our adapted human pose (AHuP) approach.
AHuP is built upon two key components: (1) semantically aware adaptation (SAA)
for the cross-domain feature space adaptation, and (2) skeletal pose adaptation
(SPA) for the pose space adaptation which takes only limited information from
the target domain. By using zero real 3D human pose data, one of our adapted
synthetic models shows comparable performance with the SOTA pose estimation
models trained with large scale real 3D human datasets. The proposed SPA can be
also employed independently as a light-weighted head to improve existing SOTA
models in a novel context. A new 3D scan-based synthetic human dataset called
ScanAva+ is also going to be publicly released with this work.
