Optimizing MPJPE promotes miscalibration in multi-hypothesis human pose lifting

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, thus providing the necessary trustworthiness in safety-critical domains. This study shows that multiple hypothesis pose estimation methods produce miscalibrated distributions. We identify that miscalibration can be attributed to the optimization of mean per joint position error MPJPE. In a series of simulations, we show th