Neural Assistive Impulses: Synthesizing Exaggerated Motions for Physics-based Characters
Summary: arXiv:2604.05394v1 Announce Type: new
Abstract: Physics-based character animation has become a fundamental approach for synthesizing realistic, physically plausible motions. While current data-driven deep reinforcement learning (DRL) methods can synthesize complex skills, they struggle to reproduce exaggerated, stylized motions, such as instantaneous dashes or mid-air trajectory changes, which are required in animation but violate standard physical laws.
The primary limitation stems from modeling the character as an underactuated floating-base system, in which internal joint torques and momentum conservation strictly govern motion. Direct attempts to enforce such motions via external wrenches often lead to training instability, as velocity discontinuities produce sparse, high-magnitude force spikes that prevent policy convergence.
Introduction to Assistive Impulse Neural Control
We propose Assistive Impulse Neural Control, a framework that reformulates external assistance in impulse space rather than force space to ensure numerical stability. This innovative approach addresses the challenges faced by existing methods in generating exaggerated animations while maintaining physical plausibility.
Key Features of the Framework
- Impulse-based Assistance: The framework utilizes impulse-based signals, which allows for more stable training and reduces the risk of encountering high-magnitude force spikes.
- Analytic High-Frequency Component: An analytic high-frequency component is derived from Inverse Dynamics, which provides a solid foundation for the motion synthesis.
- Learned Low-Frequency Residual Correction: A learned low-frequency residual correction is integrated, governed by a hybrid neural policy that refines the motion outputs.
Advantages of the Proposed Method
Our method enables robust tracking of highly agile, dynamically infeasible maneuvers that were previously intractable for physics-based methods. The advantages can be outlined as follows:
- Improved Stability: By focusing on impulses rather than forces, we achieve greater numerical stability during the training process.
- Enhanced Realism: The ability to generate exaggerated motions enhances the realism and appeal of animated characters, making them more engaging in various applications.
- Versatility: The framework can be applied across different character types and animation genres, increasing its utility in the animation industry.
Conclusion
Assistive Impulse Neural Control represents a significant advancement in the field of physics-based character animation. By addressing the limitations of existing methods through innovative impulse-based assistance, our framework not only enhances the capability to generate stylized motions but also maintains the necessary physical plausibility. This work opens new avenues for research and application in animated character design, providing animators with powerful tools to create more dynamic and engaging performances.
As the field of artificial intelligence continues to evolve, we anticipate further developments that will enhance the realism and creativity of character animations, pushing the boundaries of what is possible in animation technology.