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.

翻译：预测人类运动时对不确定性的正确定性与这一预测的准确性同样重要。 我们提出了一个新的方法来正确预测与预测未来轨迹分布相关的不确定性。 我们的CovariaceNet(CovariaceNet)方法基于一个条件性生成模型,其中含有高斯潜伏变量,以便预测双变量高斯分布的参数。 CovencyNet(CovolianceNet)和运动预测模型的结合导致一种混合方法,该方法输出单式分布。 我们将根据我们在ETH数据集(cite{pelleglegrini2009yo})中拟议并经过验证的衡量标准,显示在预测不确定性时,一些最先进的运动预测方法是如何变得过于自信的。 CovarianceNet正确地预测了不确定性,这使得我们的方法适合于使用预测分布(例如规划或决策)的应用。