with YOLOv4 [7]
DeepLabv3 [12].

lengths are removed to avoid training on erroneous anno-
tations. We use all cameras of 3DHP, and all HD cameras
of CMU-Panoptic (and all sequences with labels). We
further calibrated all cameras of BML-MoVi that did not
have calibration provided in the dataset, and use all of
them in training (based on pose predictions from an earlier
version of our model). We use 200k composited images for
MuCo-3DHP, generated with the ofﬁcial Matlab script.

S4. Evaluation Details

We evaluate all 24 SMPL joints for 3DPW, and all 17
joints for 3DHP and MuPoTS. In case of 3DPW, the entire
dataset is used for testing. For 3DHP we use the ofﬁcial
split, for H36M the most common split from the literature,
i.e. subjects S9 and S11 are used for testing.
Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats