  Due to depth ambiguities and occlusions, lifting 2D poses to 3D is a highly
ill-posed problem. Well-calibrated distributions of possible poses can make
these ambiguities explicit and preserve the resulting uncertainty for
downstream tasks. This study shows that previous attempts, which account for
these ambiguities via multiple hypotheses generation, produce miscalibrated
distributions. We identify that miscalibration can be attributed to the use of
sample-based metrics such as minMPJPE. In a series of simulations, we show that
minimizing minMPJPE, as commonly done, should converge to the correct mean
prediction. However, it fails to correctly capture the uncertainty, thus
resulting in a miscalibrated distribution. To mitigate this problem, we propose
an accurate and well-calibrated model called Conditional Graph Normalizing Flow
(cGNFs). Our model is structured such that a single cGNF can estimate both
conditional and marginal densities within the same model - effectively solving
a zero-shot density estimation problem. We evaluate cGNF on the Human~3.6M
dataset and show that cGNF provides a well-calibrated distribution estimate
while being close to state-of-the-art in terms of overall minMPJPE.
Furthermore, cGNF outperforms previous methods on occluded joints while it
remains well-calibrated.
