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 prediction of single or multiple possible futures using GANs also has precedents. In @cite_11 , the authors trained a convolution model for both the generator and the discriminator in order to predict future frames. They improved the predictions by adding an image gradient difference loss to the adversary loss. However, they again only predict a single possible future and, due to the use of the convolution network, the model can only predict a fixed length output. In contrast, we support variable length input and variable length output, and can also generate multiple possible futures from the same input.
@cite_11
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Recent progress in using recurrent neural networks (RNNs) for image description has motivated us to explore the application of RNNs to video description. Recent work has also suggested that attention mechanisms may be able to increase performance. To this end, we apply a long short-term memory (LSTM) network in two configurations: with a recently introduced soft-attention mechanism, and without. Our results suggest two things. First, incorporating a soft-attention mechanism into the text generation RNN significantly improves the quality of the descriptions. Second, using a combination of still frame features and dynamic motion-based features can also help. Ultimately, our combined approach exceeds the state-of-art on both BLEU and Meteor on the Youtube2Text dataset. We also present results on a new, larger and more complex dataset of paired video and natural language descriptions based on the use of Descriptive Video Service (DVS) annotations which are now widely available as an additional audio track on many DVDs.