camera intrinsics, 3D coordinates for some subset of the
joints, a bounding box and a segmentation mask.
Evaluation Metrics.
We evaluate on four datasets:
MuPoTS [59], 3DPW [56], 3DHP [58] and H36M [10, 32].
Over the years, different evaluation metrics and protocols
have become customary on different datasets, whose de-
tails can be very arcane. Especially in a multi-dataset set-
ting, we ﬁnd it important to use consistent metrics. For our
main experiments, we therefore adopt the following four
metrics everywhere: MPJPE: mean Euclidean distance be-
tween predicted and ground truth joints after alignment at
the root joint. PMPJPE: mean Euclidean distance after Pro-
crustes alignment. PCK@100mm: percentage of joints pre-
dicted within 100 mm of the ground truth after root align-
ment. CPS@200mm: percentage of poses where all joints
are within 200 mm distance of the ground truth after root
alignment [77]. More details on the experimental setup are
in the supplementary.
Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats