Multi-level Motion Attention for Human Motion Prediction (2106.09300v1)

Abstract: Human motion prediction aims to forecast future human poses given a historical motion. Whether based on recurrent or feed-forward neural networks, existing learning based methods fail to model the observation that human motion tends to repeat itself, even for complex sports actions and cooking activities. Here, we introduce an attention based feed-forward network that explicitly leverages this observation. In particular, instead of modeling frame-wise attention via pose similarity, we propose to extract motion attention to capture the similarity between the current motion context and the historical motion sub-sequences. In this context, we study the use of different types of attention, computed at joint, body part, and full pose levels. Aggregating the relevant past motions and processing the result with a graph convolutional network allows us to effectively exploit motion patterns from the long-term history to predict the future poses. Our experiments on Human3.6M, AMASS and 3DPW validate the benefits of our approach for both periodical and non-periodical actions. Thanks to our attention model, it yields state-of-the-art results on all three datasets. Our code is available at https://github.com/wei-mao-2019/HisRepItself.

• The paper introduces a novel multi-level motion attention mechanism that leverages historical sub-sequences via GCNs to improve prediction accuracy.
• It outperforms traditional RNN and feed-forward models on datasets like Human3.6M and AMASS by reducing prediction errors in both short- and long-term tasks.
• The approach’s hierarchical design offers versatile applications in robotics, animation, and augmented reality by effectively handling complex, repetitive human motions.

Analyzing "Multi-level Motion Attention for Human Motion Prediction"

The paper "Multi-level Motion Attention for Human Motion Prediction" addresses the critical task of accurately forecasting future human poses based on historical motion data. The authors identify a notable gap in existing models—whether utilizing Recurrent Neural Networks (RNNs) or feed-forward architectures—with respect to capturing the inherent tendency of human actions to repeat themselves over time, which is observed even in complex, non-periodic activities like sports and cooking.

Methodology Overview

The authors propose a novel approach that leverages motion repetition through an attention-based feed-forward network setup. Unlike traditional frame-wise attention models that focus on pose similarity, this research introduces motion attention, designed to discern similarities between current motion and historical motion sub-sequences. The paper examines multi-level attention mechanics across joint, body part, and full pose levels. This hierarchical structure is pivotal for understanding different scales of motion repetitiveness and enabling a more nuanced prediction model.

Motion Attention Mechanism

The paper's core contribution lies in its motion attention method. This technique involves aggregating relevant past motions and dynamically adapting them to the current motion context via a Graph Convolutional Network (GCN). The network utilizes historical sub-sequences rather than static frames, providing a rich temporal context that enhances prediction accuracy. The approach has been validated on datasets such as Human3.6M, AMASS, and 3DPW, where it demonstrates superior performance over existing methods for both periodic and non-periodic tasks.

Results and Performance

The motion attention model achieves state-of-the-art results on the datasets mentioned, outperforming prior models by training a single unified model applicable to both short-term and long-term predictions. A significant observation is the approach's robustness in handling complex motions with repetitive patterns, ultimately yielding lower prediction errors across various time horizons than other contemporary models, including LTD and LPJ.

Implications and Future Work

This work has profound implications for applications requiring human motion prediction, such as robotics, animation, and augmented reality interactions. By emphasizing motion rather than static poses, the approach aligns closely with the dynamic nature of these practices. The method's adaptability to different motion levels—full body, parts, or joints—demonstrates its versatility across various motion types and complexities.

For future developments, combining the motion attention framework with complementary strategies like progressive prediction could refine motion anticipation further, paving the way for even more precise predictions. Moreover, exploring the integration of other temporal encoding techniques and extending the framework to more diverse datasets could enhance its applicability and reliability across wider scenarios.

Overall, the paper makes a significant contribution to evolving the understanding and methodologies for human motion prediction, introducing a scalable and adaptable solution that aligns well with the iterative nature of human movement.