Convolutional Sequence to Sequence Model for Human Dynamics (1805.00655v1)

Abstract: Human motion modeling is a classic problem in computer vision and graphics. Challenges in modeling human motion include high dimensional prediction as well as extremely complicated dynamics.We present a novel approach to human motion modeling based on convolutional neural networks (CNN). The hierarchical structure of CNN makes it capable of capturing both spatial and temporal correlations effectively. In our proposed approach,a convolutional long-term encoder is used to encode the whole given motion sequence into a long-term hidden variable, which is used with a decoder to predict the remainder of the sequence. The decoder itself also has an encoder-decoder structure, in which the short-term encoder encodes a shorter sequence to a short-term hidden variable, and the spatial decoder maps the long and short-term hidden variable to motion predictions. By using such a model, we are able to capture both invariant and dynamic information of human motion, which results in more accurate predictions. Experiments show that our algorithm outperforms the state-of-the-art methods on the Human3.6M and CMU Motion Capture datasets. Our code is available at the project website.

• The paper introduces a convolutional sequence-to-sequence model that outperforms traditional RNNs in modeling complex human motion.
• It employs a dual encoder-decoder structure to effectively capture both spatial and temporal dependencies, reducing mean pose errors.
• Experimental results on Human3.6M and CMU datasets demonstrate significant improvements in long-term motion prediction accuracy.

Convolutional Sequence to Sequence Model for Human Dynamics

The research paper presents an innovative approach to human motion modeling using convolutional neural networks (CNNs), a departure from the recurrent neural network (RNN) methodologies that have been predominant in the field. Human motion prediction is paramount for applications in computer vision and robotics where interaction with humans is required—making accurate motion modeling essential for areas such as autonomous vehicle navigation, sports analysis, and medical diagnosis. The complexities involved in human motion are non-trivial, given the high dimensionality and intricate dynamics, posing substantial challenges for accurate prediction.

Approach and Methods

The authors introduce a convolutional sequence-to-sequence model that aims to encapsulate both spatial and temporal dependencies inherent in human motion. The novelty of the approach lies in employing a hierarchical CNN structure to effectively encode motion sequences into long-term and short-term representations. This architecture is composed of a convolutional long-term encoder that processes the entire sequence of human poses to encapsulate invariant motion information and a decoder structured with its own encoder-decoder configuration to predict the continuation of the sequence.

This dual encoding mechanism allows the model to separately handle long-term temporal correlations and short-term dynamics, thereby mitigating the errors typically seen with RNN-based approaches, such as convergence to an undesired mean pose. The spatial and temporal dynamics are further captured through the use of a rectangular convolutional kernel, allowing the model to predict future movements more accurately by considering interdependencies between different joints.

Experimental Outcomes

The proposed approach was rigorously evaluated on two prominent datasets: the Human3.6M and the CMU Motion Capture datasets. The empirical outcomes underscore the superiority of the CNN-based method over existing state-of-the-art models, particularly concerning long-term predictions. The CNN model demonstrated a significant reduction in mean pose problems and yielded more realistic and plausible human motion predictions. Numerical results revealed statistically significant improvements in prediction accuracy when juxtaposed against leading methodologies such as the Encoder-Recurrent-Decoder (ERD), Structural RNN (SRNN), and Residual Recurrent Neural Networks (RRNN).

Theoretical and Practical Implications

The deployment of CNNs for sequence prediction marks a pivotal shift in addressing the limitations encountered by traditional RNNs in modeling human dynamics. By exploring both spatial and temporal extraction through hierarchical convolutional structures, this methodology opens up new possibilities in the precise modeling of human biomechanics. Practically, it offers a robust framework for applications requiring human interaction and motion prediction in real-time scenarios.

Future Prospects

The authors have identified areas for future exploration, including the potential integration of additional data modalities that may provide contextual cues enhancing the predictive capacity of the model. Additionally, exploring different convolutional architectures and kernel configurations could yield further insights into optimizing the spatial-temporal dynamics captured by the network. The promising results obtained from this convolutional sequence-to-sequence paradigm suggest a wealth of opportunities for advancing human motion modeling, thereby enhancing capabilities in robotics, animation, and augmented reality, among other domains.

This work’s contribution significantly aligns with contemporary trends in machine learning where CNNs are increasingly utilized for a broad spectrum of tasks beyond traditional image classification, opening substantive avenues for future research and application development within artificial intelligence and cognitive robotics.