S5. Additional Ablations
Training Length.
In Tab. S2, we study the effect of the
length of training on the ﬁnal model performance. Clearly,
longer training can further improve the results and espe-
cially the correct pose score improves. Do note that ev-
ery new line doubles the number of training steps, so this
is expensive. Further, with long trainings we noted that
training in 16-bit ﬂoating point (FP16) precision is unsta-
ble, as the activations tend to grow out of the representable
range. The cause of this was that the convolutional ker-
nels of the backbone grow in scale during gradient descent,
since. Since they are always followed by BatchNorm in
EfﬁcientNetV2, the weight scale has no impact on the net-
work output (though it has an effect on the effective learning
rate [44]). We mitigated the problem by applying a max-
norm constraint on the convolutional kernels to keep them
from growing without bound. Some numerical instability
Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats