  The correct characterization of uncertainty when predicting human motion is
equally important as the accuracy of this prediction. We present a new method
to correctly predict the uncertainty associated with the predicted distribution
of future trajectories. Our approach, CovariaceNet, is based on a Conditional
Generative Model with Gaussian latent variables in order to predict the
parameters of a bi-variate Gaussian distribution. The combination of
CovarianceNet with a motion prediction model results in a hybrid approach that
outputs a uni-modal distribution. We will show how some state of the art
methods in motion prediction become overconfident when predicting uncertainty,
according to our proposed metric and validated in the ETH data-set
\cite{pellegrini2009you}. CovarianceNet correctly predicts uncertainty, which
makes our method suitable for applications that use predicted distributions,
e.g., planning or decision making.
