  The regression of 3D Human Pose and Shape (HPS) from an image is becoming
increasingly accurate. This makes the results useful for downstream tasks like
human action recognition or 3D graphics. Yet, no regressor is perfect, and
accuracy can be affected by ambiguous image evidence or by poses and appearance
that are unseen during training. Most current HPS regressors, however, do not
report the confidence of their outputs, meaning that downstream tasks cannot
differentiate accurate estimates from inaccurate ones. To address this, we
develop POCO, a novel framework for training HPS regressors to estimate not
only a 3D human body, but also their confidence, in a single feed-forward pass.
Specifically, POCO estimates both the 3D body pose and a per-sample variance.
The key idea is to introduce a Dual Conditioning Strategy (DCS) for regressing
uncertainty that is highly correlated to pose reconstruction quality. The POCO
framework can be applied to any HPS regressor and here we evaluate it by
modifying HMR, PARE, and CLIFF. In all cases, training the network to reason
about uncertainty helps it learn to more accurately estimate 3D pose. While
this was not our goal, the improvement is modest but consistent. Our main
motivation is to provide uncertainty estimates for downstream tasks; we
demonstrate this in two ways: (1) We use the confidence estimates to bootstrap
HPS training. Given unlabelled image data, we take the confident estimates of a
POCO-trained regressor as pseudo ground truth. Retraining with this
automatically-curated data improves accuracy. (2) We exploit uncertainty in
video pose estimation by automatically identifying uncertain frames (e.g. due
to occlusion) and inpainting these from confident frames. Code and models will
be available for research at https://poco.is.tue.mpg.de.
