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

The Mamba model has gradually garnered widespread attention in 3D human pose estimation tasks due to its linear time scaling capability and excellent expressive power. However, the Mamba model exhibits deficiencies in handling human body topological structures, as its internal state space model and one-dimensional causal convolutional network have inherent design limitations in processing global topological sequences and local structures. To address these issues, we propose the Mamba-Driven Topology Fusion framework. For global topological guidance of the Mamba, we design a Bone Aware Module to deliver directional and length guidance of human skeletons in the spherical coordinate system. To capture dependencies between local joints, we enhance the convolutional structure within the Mamba by integrating forward and backward graph convolutional networks. Additionally, a Bone-Joint Fusion Embedding and a Spatiotemporal Refinement Module are proposed to fuse global skeletal and keypoint information and extract spatiotemporal features, respectively. The proposed Mamba-Driven Topology Fusion framework effectively alleviates the Mamba model’s incompatibility with the topological structures of human keypoints. We conduct extensive experiments on the Human3.6M and MPI-INF-3DHP datasets for evaluation and comparison, and the results demonstrate that the proposed method significantly reduces computational cost while achieving higher accuracy. Our model and code are available at https://github.com/ZenghaoZheng/MDTF-3DHPE.