dataset a “skeleton”, and use “landmark” and “joint” synonymously.

and are thus equivariant to rotation and translation. We
further induce chirality equivariance (left–right symmetry)
via weight sharing [80]. We call this model an Afﬁne-
Combining Autoencoder (ACAE). We employ the ACAE in
pose estimation training as an output regularizer, to encour-
age consistent predictions. This improves prediction results
both qualitatively and quantitatively. As an alternative to the
regularization approach, we can also directly predict the la-
tent keypoints of the ACAE with a 3D pose estimator. This
latter variant avoids the need for the underlying pose esti-
mator to estimate a large number of joints, which may be
costly for some methods. In both cases, the ﬁnal predic-
tions become consistent, showing the value of our approach
in tackling multi-dataset 3D pose estimation.
Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats