CovarianceNet: Conditional Generative Model for Correct Covariance Prediction in Human Motion Prediction
Aleksey Postnikov,Aleksander Gamayunov,Gonzalo Ferrer,Aleksey Postnikov,Aleksander Gamayunov,Gonzalo Ferrer
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 ...
