(also in ﬁne-tuning, for consistency), similarly with the
teacher loss. In some datasets the absolute distance to the
person can be very large (e.g., JTA, SAIL-VOS, ASPset).
Here the absolute loss would overwhelm the total loss, so
we scale down the absolute Z component to a maximum
effective distance of 10 m for loss computation.
Batch Composition. In Tab. S1, we specify the number of
examples from each dataset per batch. This is based on the
total number of examples in each dataset but not linearly,
as we oversample smaller datasets compared to their size,
in order to provide more diverse supervision to the model.
For batch generation, we set up one queue per dataset that
iterates over epochs of that dataset, then we interleave the
streams and chunk it into batches (as opposed to indepen-
dently sampling each batch).
Initialization Details. We initialize with ImageNet-
pretrained weights. For the RN50 experiment in Table 4
(SOTA), we use ResNet50V1.5 as implemented in PyTorch,
Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats