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 has been much interest in probabilistic estimation and synthetic data generation using deep neural network architectures and learning algorithms. We propose a novel sequence-to-sequence model for probabilistic human motion prediction, trained with a modified version of improved Wasserstein generative adversarial networks (WGAN-GP), in which we use a custom loss function designed for human motion prediction. Our model, which we call HP-GAN, learns a probability density function of future human poses conditioned on previous poses. It predicts multiple sequences of possible future human poses, each from the same input sequence but a different vector z drawn from a random distribution. Furthermore, to quantify the quality of the non-deterministic predictions, we simultaneously train a motion-quality-assessment model that learns 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 and Human3.6M. We train our model on both single and multiple action types. Its predictive power for long-term motion estimation is demonstrated by generating multiple 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 the code used in this paper to Github.
The ways to compute the kinematics and dynamic quantities of human bodies in motion have been studied in many biomedical papers. This paper presents a comprehensive survey of 3D human motion editing and synthesis techniques. Firstly, four types of methods for 3D human motion synthesis are introduced and compared. Secondly, motion capture data representation, motion editing, and motion synthesis are reviewed successively. Finally, future research directions are suggested.  In this paper, we investigate whether it is possible to develop a measure that quantifies the naturalness of human motion (as defined by a large database). Such a measure might prove useful in verifying that a motion editing operation had not destroyed the naturalness of a motion capture clip or that a synthetic motion transition was within the space of those seen in natural human motion. We explore the performance of mixture of Gaussians (MoG), hidden Markov models (HMM), and switching linear dynamic systems (SLDS) on this problem. We use each of these statistical models alone and as part of an ensemble of smaller statistical models. We also implement a Naive Bayes (NB) model for a baseline comparison. We test these techniques on motion capture data held out from a database, keyframed motions, edited motions, motions with noise added, and synthetic motion transitions. We present the results as receiver operating characteristic (ROC) curves and compare the results to the judgments made by subjects in a user study. Human figures have been animated using a variety of geometric models, including stick figures, polygonal models and NURBS-based models with muscles, flexible skin or clothing. This paper reports on experimental results indicating that a viewer's perception of motion characteristics is affected by the geometric model used for rendering. Subjects were shown a series of paired motion sequences and asked if the two motions in each pair were the same or different. The motion sequences in each pair were rendered using the same geometric model. For the three types of motion variation tested, sensitivity scores indicate that subjects were better able to observe changes with the polygonal model than they were with the stick-figure model. Motion capture data and techniques for blending, editing, and sequencing that data can produce rich, realistic character animation; however, the output of these motion processing techniques sometimes appears unnatural. For example, the motion may violate physical laws or reflect unreasonable forces from the character or the environment. While problems such as these can be fixed, doing so is not yet feasible in real time environments. We are interested in developing ways to estimate perceived error in animated human motion so that the output quality of motion processing techniques can be better controlled to meet user goals.This paper presents results of a study of user sensitivity to errors in animated human motion. Errors were systematically added to human jumping motion, and the ability of subjects to detect these errors was measured. We found that users were able to detect motion with errors, and noted some interesting trends: errors in horizontal velocity were easier to detect than errors in vertical velocity, and added accelerations were easier to detect than added decelerations. On the basis of our results, we propose a perceptually based metric for measuring errors in ballistic human motion.