  In this paper, a novel Diffusion-based 3D Pose estimation (D3DP) method with
Joint-wise reProjection-based Multi-hypothesis Aggregation (JPMA) is proposed
for probabilistic 3D human pose estimation. On the one hand, D3DP generates
multiple possible 3D pose hypotheses for a single 2D observation. It gradually
diffuses the ground truth 3D poses to a random distribution, and learns a
denoiser conditioned on 2D keypoints to recover the uncontaminated 3D poses.
The proposed D3DP is compatible with existing 3D pose estimators and supports
users to balance efficiency and accuracy during inference through two
customizable parameters. On the other hand, JPMA is proposed to assemble
multiple hypotheses generated by D3DP into a single 3D pose for practical use.
It reprojects 3D pose hypotheses to the 2D camera plane, selects the best
hypothesis joint-by-joint based on the reprojection errors, and combines the
selected joints into the final pose. The proposed JPMA conducts aggregation at
the joint level and makes use of the 2D prior information, both of which have
been overlooked by previous approaches. Extensive experiments on Human3.6M and
MPI-INF-3DHP datasets show that our method outperforms the state-of-the-art
deterministic and probabilistic approaches by 1.5% and 8.9%, respectively. Code
is available at https://github.com/paTRICK-swk/D3DP.
