Deep representation learning for human motion prediction and classification (1702.07486v2)

Abstract: Generative models of 3D human motion are often restricted to a small number of activities and can therefore not generalize well to novel movements or applications. In this work we propose a deep learning framework for human motion capture data that learns a generic representation from a large corpus of motion capture data and generalizes well to new, unseen, motions. Using an encoding-decoding network that learns to predict future 3D poses from the most recent past, we extract a feature representation of human motion. Most work on deep learning for sequence prediction focuses on video and speech. Since skeletal data has a different structure, we present and evaluate different network architectures that make different assumptions about time dependencies and limb correlations. To quantify the learned features, we use the output of different layers for action classification and visualize the receptive fields of the network units. Our method outperforms the recent state of the art in skeletal motion prediction even though these use action specific training data. Our results show that deep feedforward networks, trained from a generic mocap database, can successfully be used for feature extraction from human motion data and that this representation can be used as a foundation for classification and prediction.

• The paper introduces a novel temporal encoder-decoder that learns compact representations from 3D human motion data for accurate prediction.
• The model achieves significant improvements in long-term motion prediction and action classification on CMU mocap and H3.6M datasets compared to traditional RNN and LSTM methods.
• The framework’s low computational complexity supports real-time applications while effectively capturing key motion dynamics from skeletal data.

Deep Representation Learning for Human Motion Prediction and Classification

The paper "Deep Representation Learning for Human Motion Prediction and Classification" introduces a novel deep learning framework aimed at extracting robust and generalizable features from 3D human motion capture data. The central contribution of this research is the development of a temporal encoder-decoder model that learns a compact representation from a large dataset of human motion and can effectively predict unseen motion sequences. Unlike previous methods that often require action-specific training data, this approach emphasizes a generalized model suitable for a wide array of movements and applications.

Methodological Overview

The framework employs an encoding-decoding network structure, where an encoder maps the recent past of motion data to a concise feature representation, and a decoder reconstructs future motion frames from this encoded form. This model specifically addresses the distinct characteristics of skeletal data, which differ fundamentally from video or audio sequences. Different network architectures are evaluated to understand varying temporal dependencies and limb correlations within the skeletal motions. Notably, the authors explore three variations of their temporal encoder architecture:

1. Symmetric Temporal Encoder (S-TE): This structure mirrors the encoding and decoding paths, following the traditional autoencoder schematic, to capture global motion patterns.
2. Convolutional Temporal Encoder (C-TE): Incorporating convolutions over time scales, this model captures local temporal features effectively without conflating limb correlations due to convolutional filters spanning entire limbs.
3. Hierarchical Temporal Encoder (H-TE): This design embeds the anatomical hierarchy of human joints, facilitating the learning of more nuanced representations by aligning with biological limb structures.

Empirical Results

The paper reports substantial improvements in action classification and motion prediction tasks compared to state-of-the-art methods like Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks. On benchmarks using the CMU mocap dataset and the H3.6M dataset, the proposed models particularly excel in generalizing to new, unseen actions without requiring extensive retraining. For instance, the H-TE model achieves lower prediction errors over various future time scales, showcasing its ability to maintain accuracy in long-term predictions, a notable challenge in dynamic motion systems.

Moreover, the research highlights the frameworks' low computational complexity post-training, making them well-suited for real-time applications. The feature visualization section further demonstrates how different network layers capture distinct motion attributes, supporting the model's utility in feature extraction and representation learning.

Implications and Future Directions

This work has far-reaching implications for areas such as robotics, computer vision, and human-computer interaction. By providing a structured approach to understanding and anticipating human motion, these models could enhance robotics' ability to mimic, interpret, and interact with human actions seamlessly. The learned representations also hold promise for improving motion tracking systems and enabling more sophisticated action recognition tasks.

Looking forward, several future research avenues are suggested. Integrating uncertainty quantification within the predictions could increase reliability in applications like collaborative robotics, where safety is critical. Additionally, adapting the model to handle different input forms, including real-time 2D skeletal data from RGB cameras, would broaden its applicability across industries.

In conclusion, "Deep Representation Learning for Human Motion Prediction and Classification" delivers a compelling exploration into creating a unified, efficient method for capturing, predicting, and interpreting human motion, presenting a significant step towards more intelligent systems that can navigate and interact with the dynamically complex human world.