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Predicting and understanding human motion dynamics has many applications,such as motion synthesis, augmented reality, security, and autonomous vehicles.Due to the recent success of generative adversarial networks (GAN), there hasbeen much interest in probabilistic estimation and synthetic data generationusing deep neural network architectures and learning algorithms.  We propose a novel sequence-to-sequence model for probabilistic human motionprediction, trained with a modified version of improved Wasserstein generativeadversarial networks (WGAN-GP), in which we use a custom loss function designedfor human motion prediction. Our model, which we call HP-GAN, learns aprobability density function of future human poses conditioned on previousposes. It predicts multiple sequences of possible future human poses, each fromthe same input sequence but a different vector z drawn from a randomdistribution. Furthermore, to quantify the quality of the non-deterministicpredictions, we simultaneously train a motion-quality-assessment model thatlearns the probability that a given skeleton sequence is a real human motion.  We test our algorithm on two of the largest skeleton datasets: NTURGB-D andHuman3.6M. We train our model on both single and multiple action types. Itspredictive power for long-term motion estimation is demonstrated by generatingmultiple plausible futures of more than 30 frames from just 10 frames of input.We show that most sequences generated from the same input have more than 50\%probabilities of being judged as a real human sequence. We will release all thecode used in this paper to Github.

HP-GAN: Probabilistic 3D human motion prediction via GAN