MHDPose: Multi-hypothesis 3D human pose estimation using bidirectional Mamba diffusion models

Monocular 3D human pose estimation often faces challenges due to depth ambiguities, unstable predictions, and occlusions. Diffusion models have emerged as a generative framework that can transform noise into complex data representations. Although diffusion-based multi-hypothesis approaches have been explored in prior works, their effectiveness largely depends on the denoising network, which captures spatial and long-range temporal dependencies. In this paper, we propose MHDPose, a conditional diffusion-based human pose estimation framework that can generate multiple 3D pose predictions with the guidance of a single 2D pose. The pose hypotheses are generated by the proposed PMamba denoiser that consists of: (i) a spatial transformer over joints with proposed kinematic-aware rotary position embeddings (Kin-RoPE) to encode the skeleton’s tree structure, and (ii) bi-directional Mamba blocks for efficient long-range temporal modeling, and a residual Mamba head for pose refinement. Our proposed MHDPose achieves competitive results on widely used pose estimation benchmarks such as Human3.6M and MPI-INF-3DHP.