Mamba-Driven Topology Fusion for monocular 3D human pose estimation

Key Points

• Improved accuracy in monocular 3D human pose estimation was observed, enhancing identification of joint movements effectively.
• The implementation of a mamba-driven algorithm increased estimation precision by 20% on benchmark datasets, particularly for occluded poses.
• Analysis of human poses was conducted through innovative topology fusion techniques, allowing for better integration of diverse data sources.
• This approach may enable more reliable human-computer interaction, advancing applications in animation and virtual reality.