  Monocular 3D human pose estimation from RGB images has attracted significant
attention in recent years. However, recent models depend on supervised training
with 3D pose ground truth data or known pose priors for their target domains.
3D pose data is typically collected with motion capture devices, severely
limiting their applicability. In this paper, we present a heuristic weakly
supervised 3D human pose (HW-HuP) solution to estimate 3D poses in when no
ground truth 3D pose data is available. HW-HuP learns partial pose priors from
3D human pose datasets and uses easy-to-access observations from the target
domain to estimate 3D human pose and shape in an optimization and regression
cycle. We employ depth data for weak supervision during training, but not
inference. We show that HW-HuP meaningfully improves upon state-of-the-art
models in two practical settings where 3D pose data can hardly be obtained:
human poses in bed, and infant poses in the wild. Furthermore, we show that
HW-HuP retains comparable performance to cutting-edge models on public
benchmarks, even when such models train on 3D pose data.
