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.

本文提出了 DiffusionPose，将 2D 人体姿态估计问题定义为噪声热图的关键点热图生成问题，并通过添加噪声，将关键点扩散到随机分布中，学习扩散模型从噪声热图中恢复与图像特征相关的地面真值热图，从而实现从初始化热图的漸進式降噪方式生成热图。此外，本文还进一步探究了从人体结构信息中提取条件以提高 DiffusionPose 的性能。经过广泛的实验，证明了 DiffusionPose 的优越性，在广泛使用的 COCO、CrowdPose 和 AI Challenge 数据集上分别提高了 1.6、1.2 和 1.2 mAP。