HP-GAN: Probabilistic 3D human motion prediction via GAN (1711.09561v1)

Abstract: 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 paper introduces HP-GAN, a novel GAN-based model that predicts multiple plausible 3D human motion sequences from limited past frames using a probabilistic approach.
• It employs an improved WGAN-GP with custom loss functions to ensure motion consistency and address inherent uncertainty in human motion forecasting.
• Results show over 50% of generated sequences are identified as genuine by the integrated motion-quality assessment, demonstrating robust performance across datasets.

Probabilistic 3D Human Motion Prediction via GAN

The research paper titled "HP-GAN: Probabilistic 3D human motion prediction via GAN" discusses a novel approach for predicting multiple possible future human motion sequences using a specialized GAN architecture. Human motion prediction carries significant implications in domains such as autonomous vehicles, security, augmented reality, and more. This paper addresses the uncertainty inherent in motion forecasting by utilizing a probabilistic framework rather than deterministic pathways.

The authors introduce HP-GAN, a sequence-to-sequence model based on the improved Wasserstein Generative Adversarial Network (WGAN-GP) with a custom loss function tuned for human motion prediction. The framework learns a probability density function of future human poses conditioned on previous ones. Unlike deterministic models, HP-GAN generates multiple plausible future sequences from the same past context by varying a random input vector $z$. The paper explores novel applications of GANs in probabilistic motion prediction by training the model on large skeleton datasets such as NTURGB-D and Human3.6M. Notably, the model demonstrates the capability to generate up to 30 plausible future frames from a mere 10 input frames.

For optimization, the paper leverages an adversarial training mechanism. The HP-GAN architecture incorporates a generator using an RNN-based sequence-to-sequence model and a critic network that feeds into a multilayer perceptron (MLP). A discriminator complements the setup, independently evaluating the realism of generated sequences without affecting the generator's training. This unique combination of WGAN-GP, custom-consistency losses, and bone length corrections addresses common GAN training instability issues, allowing the system to produce plausible human motion sequences.

The contributions of HP-GAN are multifaceted:

• Generation of Multiple Sequences: It efficiently predicts multiple possible future sequences from the same input, showcasing the extent of probabilistic prediction.
• Quality Assessment Model: By integrating a motion-quality-assessment model, the system can robustly evaluate the authenticity of the generated sequences against human motion standards.
• Cross-Platform Validity: The model showcases adaptability by producing effective predictions across different modalities and datasets.

The results indicate superior performance where predicted sequences share more than 50% probability of being classified as genuine motion by the discriminator. The paper highlights the computational robustness of HP-GAN through extensive tests, confirming utility across various action types irrespective of data source imperfections or modality differences.

From a theoretical perspective, the HP-GAN architecture provides an innovative approach to address the challenges of probabilistic human motion forecasting, paving the way for advanced research in human-machine interaction, predictive analytics, and synthetic data generation. Practically, the ability to predict a spectrum of potential motion outcomes presents significant advantages in environments requiring dynamic human interaction forecasting such as robotics, safety systems, and interactive entertainment.

In future work, optimizing the convergence metrics and stability benchmarks for training GAN models remain areas for further inquiry. Understanding the latent space represented by $z$ for application in classification and clustering could serve to expand its utility beyond prediction into broader analytical applications. Furthermore, leveraging the predictive outputs for data augmentation could enhance the versatility of machine learning models in the context of human motion understanding.

Overall, this paper makes a pivotal contribution to the ongoing development of robust, probabilistic models for predictive human motion analysis, offering a comprehensive solution to previously noted limitations in deterministic forecasting methodologies.