  One of the mainstream schemes for 2D human pose estimation (HPE) is learning
keypoints heatmaps by a neural network. Existing methods typically improve the
quality of heatmaps by customized architectures, such as high-resolution
representation and vision Transformers. In this paper, we propose
\textbf{DiffusionPose}, a new scheme that formulates 2D HPE as a keypoints
heatmaps generation problem from noised heatmaps. During training, the
keypoints are diffused to random distribution by adding noises and the
diffusion model learns to recover ground-truth heatmaps from noised heatmaps
with respect to conditions constructed by image feature. During inference, the
diffusion model generates heatmaps from initialized heatmaps in a progressive
denoising way. Moreover, we further explore improving the performance of
DiffusionPose with conditions from human structural information. Extensive
experiments show the prowess of our DiffusionPose, with improvements of 1.6,
1.2, and 1.2 mAP on widely-used COCO, CrowdPose, and AI Challenge datasets,
respectively.
