Monocular 3D human pose estimation is quite challenging due to the inherent
ambiguity and occlusion, which often lead to high uncertainty and
indeterminacy. On the other hand, diffusion models have recently emerged as an
effective tool for generating high-quality images from noise. Inspired by their
capability, we explore a novel pose estimation framework (DiffPose) that
formulates 3D pose estimation as a reverse diffusion process. We incorporate
novel designs into our DiffPose to facilitate the diffusion process for 3D pose
estimation: a pose-specific initialization of pose uncertainty distributions, a
Gaussian Mixture Model-based forward diffusion process, and a
context-conditioned reverse diffusion process. Our proposed DiffPose
significantly outperforms existing methods on the widely used pose estimation
benchmarks Human3.6M and MPI-INF-3DHP. Project page:
this https URL

提出一种新颖的姿态估计框架 (DiffPose)，基于扩散模型将 3D 姿态估计描述为一个逆扩散过程，并引入了姿态特定初始化、基于高斯混合模型的前向扩散过程和上下文条件的逆扩散过程等新设计来促进扩散过程，相比现有方法在 Human3.6M 和 MPI-INF-3DHP 等广泛使用的姿态估计基准测试中显著提升。