Human motion prediction model has applications in various fields of computer vision. Without taking into account the inherent stochasticity in the prediction of future pose dynamics, such methods often converges to a deterministic undesired mean of multiple probable outcomes. Devoid of this, we propose a novel probabilistic generative approach called Bidirectional Human motion prediction GAN, or BiHMP-GAN. To be able to generate multiple probable human-pose sequences, conditioned on a given starting sequence, we introduce a random extrinsic factor r, drawn from a predefined prior distribution. Furthermore, to enforce a direct content loss on the predicted motion sequence and also to avoid mode-collapse, a novel bidirectional framework is incorporated by modifying the usual discriminator architecture. The discriminator is trained also to regress this extrinsic factor r, which is used alongside with the intrinsic factor (encoded starting pose sequence) to generate a particular pose sequence. To further regularize the training, we introduce a novel recursive prediction strategy. In spite of being in a probabilistic framework, the enhanced discriminator architecture allows predictions of an intermediate part of pose sequence to be used as a conditioning for prediction of the latter part of the sequence. The bidirectional setup also provides a new direction to evaluate the prediction quality against a given test sequence. For a fair assessment of BiHMP-GAN, we report performance of the generated motion sequence using (i) a critic model trained to discriminate between real and fake motion sequence, and (ii) an action classifier trained on real human motion dynamics. Outcomes of both qualitative and quantitative evaluations, on the probabilistic generations of the model, demonstrate the superiority of BiHMP-GAN over previously available methods.
Data-driven human motion prediction models have been explored by researchers for quite a along time in both computer animation and machine learning community. Before the deep-era various probabilistic graphical models have been tried to efficiently model human motion dynamics. Researchers have used time-series learning methods like Hidden Markov Model @cite_17 , restricted Boltzmann machines @cite_19 , Gaussian process @cite_14 , switching linear dynamical system @cite_12 to model human pose sequence data. However these methods fail to model the high-dimensional complex human pose sequence information effectively. Because of the highly nonlinear dependencies arose by the uncertainty in human movement, individually modeling various different factors affecting motion prediction does not scale well. These methods also suffer from complex training regime @cite_19 with complicated inference pipeline as a result of the acquired sampling technique.
@cite_19 @cite_14 @cite_12 @cite_17
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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 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. Abstract We describe algorithms for recognizing human motion in monocular video sequences, based on discriminative conditional random fields (CRFs) and maximum entropy Markov models (MEMMs). Existing approaches to this problem typically use generative structures like the hidden Markov model (HMM). Therefore, they have to make simplifying, often unrealistic assumptions on the conditional independence of observations given the motion class labels and cannot accommodate rich overlapping features of the observation or long-term contextual dependencies among observations at multiple timesteps. This makes them prone to myopic failures in recognizing many human motions, because even the transition between simple human activities naturally has temporal segments of ambiguity and overlap. The correct interpretation of these sequences requires more holistic, contextual decisions, where the estimate of an activity at a particular timestep could be constrained by longer windows of observations, prior and even posterior to that timestep. This would not be computationally feasible with a HMM which requires the enumeration of a number of observation sequences exponential in the size of the context window. In this work we follow a different philosophy: instead of restrictively modeling the complex image generation process – the observation, we work with models that can unrestrictedly take it as an input, hence condition on it. Conditional models like the proposed CRFs seamlessly represent contextual dependencies and have computationally attractive properties: they support efficient, exact recognition using dynamic programming, and their parameters can be learned using convex optimization. We introduce conditional graphical models as complementary tools for human motion recognition and present an extensive set of experiments that show not only how these can successfully classify diverse human activities like walking, jumping, running, picking or dancing, but also how they can discriminate among subtle motion styles like normal walks and wander walks. We propose a Bayesian nonparametric approach to the problem of jointly modeling multiple related time series. Our model discovers a latent set of dynamical behaviors shared among the sequences, and segments each time series into regions defined by a subset of these behaviors. Using a beta process prior, the size of the behavior set and the sharing pattern are both inferred from data. We develop Markov chain Monte Carlo (MCMC) methods based on the Indian buffet process representation of the predictive distribution of the beta process. Our MCMC inference algorithm efficiently adds and removes behaviors via novel split-merge moves as well as data-driven birth and death proposals, avoiding the need to consider a truncated model. We demonstrate promising results on unsupervised segmentation of human motion capture data. 1. Introduction. Classical time series analysis has generally focused on the study of a single (potentially multivariate) time series. Instead, we consider analyzing collections of related time series, motivated by the increasing abundance of such data in many domains. In this work we explore this problem by considering time series produced by motion capture sensors on the joints of people performing exercise routines. An individual recording provides a multivariate time series that can be segmented into types of exercises (e.g., jumping jacks, arm-circles, and twists). Each exercise type describes locally coherent and simple dynamics that persist over a segment of time. We have such motion capture recordings from multiple individuals, each of whom performs some subset of a global set of exercises, as shown in Figure1. Our goal is to discover the set of global exercise types (“behaviors”) and their occurrences in each individual’s data stream. We would like to take advantage of the overlap between individuals: if a jumping-jack behavior is discovered in one sequence, then it can be used to model data for other individuals. We present algorithms for recognizing human motion in monocular video sequences, based on discriminative conditional random field (CRF) and maximum entropy Markov models (MEMM). Existing approaches to this problem typically use generative (joint) structures like the hidden Markov model (HMM). Therefore they have to make simplifying, often unrealistic assumptions on the conditional independence of observations given the motion class labels and cannot accommodate overlapping features or long term contextual dependencies in the observation sequence. In contrast, conditional models like the CRFs seamlessly represent contextual dependencies, support efficient, exact inference using dynamic programming, and their parameters can be trained using convex optimization. We introduce conditional graphical models as complementary tools for human motion recognition and present an extensive set of experiments that show how these typically outperform HMMs in classifying not only diverse human activities like walking, jumping. running, picking or dancing, but also for discriminating among subtle motion styles like normal walk and wander walk