  Recently, there has been an arms race of pose forecasting methods aimed at
solving the spatio-temporal task of predicting a sequence of future 3D poses of
a person given a sequence of past observed ones. However, the lack of unified
benchmarks and limited uncertainty analysis have hindered progress in the
field. To address this, we first develop an open-source library for human pose
forecasting, including multiple models, supporting several datasets, and
employing standardized evaluation metrics, with the aim of promoting research
and moving toward a unified and consistent evaluation. Second, we devise two
types of uncertainty in the problem to increase performance and convey better
trust: 1) we propose a method for modeling aleatoric uncertainty by using
uncertainty priors to inject knowledge about the pattern of uncertainty. This
focuses the capacity of the model in the direction of more meaningful
supervision while reducing the number of learned parameters and improving
stability; 2) we introduce a novel approach for quantifying the epistemic
uncertainty of any model through clustering and measuring the entropy of its
assignments. Our experiments demonstrate up to $25\%$ improvements in
forecasting at short horizons, with no loss on longer horizons on Human3.6M,
AMSS, and 3DPW datasets, and better performance in uncertainty estimation. The
code is available online at https://github.com/vita-epfl/UnPOSed.
