1

,

4. Experimental Setup

Base Model. We adopt the recent state-of-the-art Me-
TRAbs [70] 3D human pose estimator as the platform for
our experiments, but we note that our method is agnostic to
the speciﬁcs of the underlying pose estimator. Unless men-
tioned otherwise, the backbone is EfﬁcientNetV2-S [73].
Training Details. We perform 400k training steps with
AdamW [53] and batch size 128, with every dataset repre-
sented with a ﬁxed number of examples per batch. Batch
composition and learning rate schedule are speciﬁed in the
supplementary. We use ghost BatchNorm [27, 71] with size
16, as this improved convergence in multi-dataset training,
together with switching the BatchNorm layers to inference
mode for the last 1000 updates. The ﬁnal ﬁne-tuning phase
has 40k iterations with smaller learning rate on the back-
bone than the heads. The autoencoder weights are trained

on pseudo-GT obtained with EffV2-L. We crop a 256×256
Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats