In the field of digital replication of human motion, researchers have long faced two main challenges: the computational complexities of these models, and capturing the intricate, fluid nature of human movement. Utilising state space models, particularly the Mamba variant, has yielded promising advancements in handling long sequences more effectively while reducing computational demands. However, these models still lack the ability to fully capture the minute details and overall integrity of human movement.
Addressing these challenges, a team of researchers from Monash University, The Australian National University, Mohamed bin Zayed University of Artificial Intelligence, and Carnegie Mellon University have introduced Motion Mamba. This model distinguishes itself by integrating two mechanisms: the Hierarchical Temporal Mamba (HTM) block and the Bidirectional Spatial Mamba (BSM) block.
The HTM block operates by analyzing temporal aspects of movements, using a hierarchical scanning approach to interpret complex movement patterns over time. The BSM block on the other hand, deals with spatial data. By processing information in both forward and reverse directions, it ensures a thorough understanding of any given motion.
In terms of performance, Motion Mamba outshines existing methods by achieving up to 50% better Fréchet Inception Distance (FID) scores, indicating its superior capability to generate high-quality, realistic human motion sequences. Additionally, Motion Mamba’s design allows for processing speeds up to four times faster than current methods.
To summarize, the key highlights of the research are:
– State space models, particularly the Mamba variant, are being explored to improve digital human motion replication.
– Motion Mamba, an evolution of the Mamba model, has been introduced, integrating Hierarchical Temporal Mamba (HTM) and Bidirectional Spatial Mamba (BSM) blocks.
– This new model can handle both temporal and spatial complexities of human motion.
– Motion Mamba has proven exceptionally efficient, much faster, and highly accurate with a 50% better FID score than existing models.
This work can be explored further through the research paper and Github. Follow-ups on further advancements can be found on their Twitter, Telegram Channel, Discord Channel, and LinkedIn Group.
