  Many human pose estimation methods estimate Skinned Multi-Person Linear
(SMPL) models and regress the human joints from these SMPL estimates. In this
work, we show that the most widely used SMPL-to-joint linear layer (joint
regressor) is inaccurate, which may mislead pose evaluation results. To achieve
a more accurate joint regressor, we propose a method to create
pseudo-ground-truth SMPL poses, which can then be used to train an improved
regressor. Specifically, we optimize SMPL estimates coming from a
state-of-the-art method so that its projection matches the silhouettes of
humans in the scene, as well as the ground-truth 2D joint locations. While the
quality of this pseudo-ground-truth is challenging to assess due to the lack of
actual ground-truth SMPL, with the Human 3.6m dataset, we qualitatively show
that our joint locations are more accurate and that our regressor leads to
improved pose estimations results on the test set without any need for
retraining. We release our code and joint regressor at
https://github.com/ubc-vision/joint-regressor-refinement
