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MHDPose: Multi-hypothesis 3D human pose estimation using bidirectional Mamba diffusion models
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DOI：10.1016/j.patcog.2026.114396.png)
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Abstract
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• We propose a diffusion-based multi-hypothesis framework for 3D human pose estimation. • We designed a hybrid denoiser architecture combining transformer and state space model. • We introduce kinematic-aware spatial encoding to improve joint reasoning. • We design a probabilistic approach for generating diverse 3D poses from 2D observations. • Experimental results show improved accuracy on benchmark datasets.
Keywords:
Human pose estimation
Diffusion
Mamba
State Space Models
Multi-hypothesis generation
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