Motion Mamba: Efficient and Long Sequence Motion Generation with Hierarchical and Bidirectional Selective SSM

Motion Mamba: Efficient and Long Sequence Motion Generation with Hierarchical and Bidirectional Selective SSM

Overview

The paper proposes "Motion Mamba," a novel approach for efficient and long-sequence motion generation using hierarchical and bidirectional selective State Space Models (SSMs). This method addresses the challenges faced by current models in generating long-duration human motion sequences by incorporating a new architecture inspired by recent advancements in SSMs, particularly the Mamba model. The authors introduce hierarchical temporal and bidirectional spatial processing blocks, enhancing the model's capability to maintain motion consistency and accurately capture motion dynamics over extended sequences.

Technical Contributions

The paper's primary contributions include the following:

Hierarchical Temporal Mamba (HTM) Block:

• The HTM block processes temporal data using different numbers of isolated SSM modules across a symmetric U-Net architecture, enhancing motion consistency between frames.
• A hierarchical scanning sequence ${S^{2N-1}, \ldots, S^1}$ is employed, with the scan complexity descending from higher to lower levels to manage motion detail density efficiently.

Bidirectional Spatial Mamba (BSM) Block:

• The BSM block processes latent poses bidirectionally to refine motion accuracy within a temporal frame.
• This block maintains continuity of information flow, which significantly improves the model’s ability to generate precise motions.

Efficient Architecture:

• By leveraging the reduced computational complexity of selective operations, the Motion Mamba model achieves remarkable efficiency, yielding faster inference speeds.

Experimental Results

Evaluations were conducted on the HumanML3D and KIT-ML datasets, comparing Motion Mamba with state-of-the-art methods. The key findings include:

• Fréchet Inception Distance (FID): Motion Mamba improves FID by up to 50%, indicating superior generation quality.
• Inference Speed: The model demonstrates up to four times faster inference compared to previous methods, achieving an average inference time of 0.058 seconds per sequence on the HumanML3D dataset.
• Long Sequence Modeling: The model excels in generating long-duration sequences, highlighted by tests on the HumanML3D-LS subset.

Comparative Analysis

The paper contrasts Motion Mamba against leading methods like MLD, MotionDiffuse, and MDM. The results highlight Motion Mamba's improvements in key metrics:

• R Precision: Achieves top-1 accuracy of 0.502 on HumanML3D, outperforming other models.
• Multi-Modal Distance (MM Dist): Records ≤3.060, indicating enhanced text-motion alignment.
• Diversity and MModality: Demonstrates high diversity and multimodal capacity, ensuring varied generation.

Practical and Theoretical Implications

The strong numerical results suggest practical applications in areas requiring realistic and coherent human motion generation, such as computer animation, game development, and robotic control. The hierarchical and bidirectional selective SSM framework sets a precedent for future research in efficiently handling long-range dependencies in generative models. Potential future developments could involve exploring further hierarchical arrangements and combining SSMs with emerging technologies in neural architecture search and adaptive learning.

Conclusion

Motion Mamba represents a significant advancement in human motion generation, balancing accuracy and efficiency through innovative hierarchical and bidirectional design elements. By integrating selective SSMs within a U-Net architecture, the model achieves state-of-the-art performance in generating realistic long-sequence motions, offering valuable insights and methodologies for future research in generative computer vision.