  Previous probabilistic models for 3D Human Pose Estimation (3DHPE) aimed to
enhance pose accuracy by generating multiple hypotheses. However, most of the
hypotheses generated deviate substantially from the true pose. Compared to
deterministic models, the excessive uncertainty in probabilistic models leads
to weaker performance in single-hypothesis prediction. To address these two
challenges, we propose a diffusion-based refinement framework called DRPose,
which refines the output of deterministic models by reverse diffusion and
achieves more suitable multi-hypothesis prediction for the current pose
benchmark by multi-step refinement with multiple noises. To this end, we
propose a Scalable Graph Convolution Transformer (SGCT) and a Pose Refinement
Module (PRM) for denoising and refining. Extensive experiments on Human3.6M and
MPI-INF-3DHP datasets demonstrate that our method achieves state-of-the-art
performance on both single and multi-hypothesis 3DHPE. Code is available at
https://github.com/KHB1698/DRPose.
