Multi-task Deep Learning for Real-Time 3D Human Pose Estimation and Action Recognition (1912.08077v2)

Abstract: Human pose estimation and action recognition are related tasks since both problems are strongly dependent on the human body representation and analysis. Nonetheless, most recent methods in the literature handle the two problems separately. In this work, we propose a multi-task framework for jointly estimating 2D or 3D human poses from monocular color images and classifying human actions from video sequences. We show that a single architecture can be used to solve both problems in an efficient way and still achieves state-of-the-art or comparable results at each task while running at more than 100 frames per second. The proposed method benefits from high parameters sharing between the two tasks by unifying still images and video clips processing in a single pipeline, allowing the model to be trained with data from different categories simultaneously and in a seamlessly way. Additionally, we provide important insights for end-to-end training the proposed multi-task model by decoupling key prediction parts, which consistently leads to better accuracy on both tasks. The reported results on four datasets (MPII, Human3.6M, Penn Action and NTU RGB+D) demonstrate the effectiveness of our method on the targeted tasks. Our source code and trained weights are publicly available at https://github.com/dluvizon/deephar.

• The paper introduces a unified architecture that integrates 2D and 3D pose estimation with action recognition while processing over 100 frames per second.
• It employs a differentiable soft-argmax method that ensures end-to-end gradient flow for high-precision joint estimation.
• Decoupling training for pose and action tasks enhances accuracy, achieving 48.6 mm error on Human3.6M and an 89.9% recognition rate on NTU RGB+D.

Multi-task Deep Learning for Real-Time 3D Human Pose Estimation and Action Recognition

This paper introduces an efficient multi-task framework designed to jointly address 3D human pose estimation and action recognition using monocular RGB images. The authors propose a single architecture capable of capturing and processing visual data to deliver real-time predictions while maintaining high degrees of accuracy for both distinguishing human postures and actions.

Key Methodological Contributions

1. Unified Architecture: The paper describes a unified deep learning framework that seamlessly integrates 2D and 3D pose estimation and action recognition. This is achieved through multi-task learning, allowing the architecture to leverage shared features between tasks, enhancing the system's overall efficiency with throughput exceeding 100 frames per second.
2. Differentiable Soft-argmax for Pose Estimation: To ensure end-to-end learning, the authors extend the differentiable soft-argmax technique to handle both 2D and 3D joint estimation. This approach obviates the need for argmax operations, which traditionally break backpropagation, thereby facilitating continuous gradient flow throughout the network.
3. Decoupling Key Prediction Parts: The framework introduces a decoupling mechanism within its training process that optimizes different components independently. By separating pose and action predictions, the model achieves enhanced precision for each task.
4. Data Utilization and Experiments: The system benefits from training with datasets like MPII, Human3.6M, Penn Action, and NTU RGB+D, which provide diverse scenarios and data points reflecting real-world applications. The multi-task model shows effective generalization across different datasets.
5. Efficiency and Scalability: Designed to accommodate the shifting balance between speed and accuracy, the architecture can be modified post-training to deliver customized performance, thereby achieving over 180 frames per second for specific configurations.

Numerical Results and Claims

• The proposed method achieves state-of-the-art results on several datasets, notably improving accuracy on 3D pose estimates and action recognition tasks.
• The average prediction error on the Human3.6M dataset is reported at 48.6 millimeters, positioning this work ahead of previous methodologies concerning pose accuracy.
• For the action recognition on the NTU RGB+D dataset, the framework achieves a 3.3% improvement over earlier methods with a success rate of 89.9%.

Theoretical and Practical Implications

The paper offers notable contributions to the fields of computer vision and human-computer interaction. By robustly integrating pose reconstruction with action interpretation, the framework could enhance human-machine collaboration, surveillance systems, and even contribute to developments in virtual and augmented reality environments. Future research opportunities might include extending this approach to incorporate temporal dynamics more profoundly or improving generalization to unseen environments and poses.

Conclusion

This paper presents a compelling approach to joint human pose estimation and action recognition using cutting-edge deep learning techniques. While achieving notable numerical results and operational efficiency, the outlined methodology demonstrates a potential leap in practical applications where real-time processing and high accuracy are paramount. Further exploration could expand this work's applicability, particularly in domains involving complex human interactions.