MHDPose: Multi-hypothesis 3D human pose estimation using bidirectional Mamba diffusion models
Marsha Mariya Kappan, Eduardo Benítez Sandoval, Erik Meijering, Francisco Cruz · Pattern Recognition · 2026
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.