  Human motion prediction and understanding is a challenging problem. Due to
the complex dynamic of human motion and the non-deterministic aspect of future
prediction. We propose a novel sequence-to-sequence model for human motion
prediction and feature learning, trained with a modified version of generative
adversarial network, with a custom loss function that takes inspiration from
human motion animation and can control the variation between multiple predicted
motion from the same input poses.
  Our model learns to predict multiple future sequences of human poses from the
same input sequence. We show that the discriminator learns general presentation
of human motion by using the learned feature in action recognition task.
Furthermore, to quantify the quality of the non-deterministic predictions, we
simultaneously train a motion-quality-assessment network that learns the
probability that a given sequence of poses is a real human motion or not.
  We test our model on two of the largest human pose datasets: NTURGB-D and
Human3.6M. We train on both single and multiple action types. Its predictive
power for motion estimation is demonstrated by generating multiple plausible
futures from the same input and show the effect of each of the loss functions.
Furthermore, we show that it takes less than half the number of epochs to train
an activity recognition network by using the feature learned from the
discriminator.
