Modeling Human Motion with Quaternion-based Neural Networks (1901.07677v2)

Abstract: Previous work on predicting or generating 3D human pose sequences regresses either joint rotations or joint positions. The former strategy is prone to error accumulation along the kinematic chain, as well as discontinuities when using Euler angles or exponential maps as parameterizations. The latter requires re-projection onto skeleton constraints to avoid bone stretching and invalid configurations. This work addresses both limitations. QuaterNet represents rotations with quaternions and our loss function performs forward kinematics on a skeleton to penalize absolute position errors instead of angle errors. We investigate both recurrent and convolutional architectures and evaluate on short-term prediction and long-term generation. For the latter, our approach is qualitatively judged as realistic as recent neural strategies from the graphics literature. Our experiments compare quaternions to Euler angles as well as exponential maps and show that only a very short context is required to make reliable future predictions. Finally, we show that the standard evaluation protocol for Human3.6M produces high variance results and we propose a simple solution.

• The paper introduces quaternion-based neural networks for modeling human motion, effectively addressing limitations like singularities and discontinuities found in traditional parameterizations.
• The methodology explores RNNs and CNNs with quaternion parameterization, utilizing a novel loss function penalizing positional errors for better kinematic representation and constraint maintenance.
• Experiments show improved performance over baselines in motion prediction and generation tasks, highlighting potential applications in graphics, robotics, and virtual simulations, while also improving standard evaluation protocols.

Quaternion-based Neural Networks for Human Motion Modeling

The paper presented by Pavllo et al. introduces a novel approach for modeling human motion using neural networks parameterized by quaternions. It provides a comprehensive evaluation across both short-term prediction and long-term generation tasks, addressing limitations in the current literature.

Key Contributions

The paper identifies critical deficiencies in existing methods for human motion modeling, particularly those that rely on Euler angles or exponential maps for parameterizing joint rotations. These traditional approaches suffer from various issues, such as discontinuities, singularities, and non-unique representations. The authors propose the use of quaternions to overcome these limitations, highlighting their advantages in terms of smooth interpolation, numerical stability, and efficient computation.

Through rigorous experimentation, the paper demonstrates quaternion-based neural network architectures outperform competitive baselines across a range of tasks. For short-term predictions, quaternions exhibit improvement over traditional Euler parameterizations and recent adversarial approaches, maintaining robustness while employing scheduled sampling techniques. Remarkably, a very short context is shown to suffice for making reliable predictions, underscoring the efficiency of the quaternion representation in capturing necessary kinematic dependencies.

Methodology

The paper investigates both recurrent neural networks (RNNs) and convolutional neural networks (CNNs) using quaternion-based parameterization, providing new insights into architectural choices for human motion modeling. They propose a unique loss function that processes kinematics through forward mapping, which penalizes positional errors and enables a constrained skeleton with proper credit assignment across joints—a notable advantage over direct angle-based loss formulations.

Long-term generation tasks are tested through a human paper that establishes the superiority of the proposed method in generating realistic motion trajectories with online efficiency. The integration with a "pace network" allows fine control over gait parameters, successfully blending artistry with technical precision—a valuable feature for applications in computer graphics and animation.

Protocol Improvements

Noteworthy is the critique and improvement of the standard evaluation protocol for the Human3.6M dataset. By substantially increasing the number of samples per sequence in evaluation, they demonstrate a significant reduction in variance and enhanced reliability of results—a meaningful contribution to evaluation methodologies in the field.

Implications and Future Directions

The paper's findings promise significant implications for both practical and theoretical developments in artificial intelligence. Quaternion-based models offer a pathway to more flexible, stable, and efficient motion modeling systems. Their potential applications span beyond human motion modeling, offering insights into rotation-heavy computations in robotics, aerospace, and virtual simulations.

The research opens avenues for further exploration into quaternion-valued neural networks, drawing potential benefits from enhanced representation capabilities over traditional real-valued networks. Moreover, the application of adversarial training methodologies in conjunction with quaternion parameterization presents an intriguing prospect to refine generative capability further.

By setting a solid groundwork with innovative parameterization and evaluation strategies, the research facilitates future work towards richer, more robust models for human motion prediction and generation, laying a foundation for exploring cross-domain applications and inspiring subsequent methodological advances.

Overall, Pavllo et al.'s paper represents a significant milestone in human motion modeling, advocating for a quaternion-based approach that presents compelling improvements over existing techniques, evaluated through an insightful blend of architectural design, methodological rigor, and experimental validation.