  Human motion prediction is a fundamental part of many human-robot
applications. Despite the recent progress in human motion prediction, most
studies simplify the problem by predicting the human motion relative to a fixed
joint and/or only limit their model to predict one possible future motion.
While due to the complex nature of human motion, a single output cannot reflect
all the possible actions one can do. Also, for any robotics application, we
need the full human motion including the user trajectory not a 3d pose relative
to the hip joint.
  In this paper, we try to address these two issues by proposing a
transformer-based generative model for forecasting multiple diverse human
motions. Our model generates \textit{N} future possible motion by querying a
history of human motion. Our model first predicts the pose of the body relative
to the hip joint. Then the \textit{Hip Prediction Module} predicts the
trajectory of the hip movement for each predicted pose frame. To emphasize on
the diverse future motions we introduce a similarity loss that penalizes the
pairwise sample distance. We show that our system outperforms the
state-of-the-art in human motion prediction while it can predict diverse
multi-motion future trajectories with hip movements
