  Recently, diffusion-based methods for monocular 3D human pose estimation have
achieved state-of-the-art (SOTA) performance by directly regressing the 3D
joint coordinates from the 2D pose sequence. Although some methods decompose
the task into bone length and bone direction prediction based on the human
anatomical skeleton to explicitly incorporate more human body prior
constraints, the performance of these methods is significantly lower than that
of the SOTA diffusion-based methods. This can be attributed to the tree
structure of the human skeleton. Direct application of the disentangled method
could amplify the accumulation of hierarchical errors, propagating through each
hierarchy. Meanwhile, the hierarchical information has not been fully explored
by the previous methods. To address these problems, a Disentangled
Diffusion-based 3D Human Pose Estimation method with Hierarchical Spatial and
Temporal Denoiser is proposed, termed DDHPose. In our approach: (1) We
disentangle the 3D pose and diffuse the bone length and bone direction during
the forward process of the diffusion model to effectively model the human pose
prior. A disentanglement loss is proposed to supervise diffusion model
learning. (2) For the reverse process, we propose Hierarchical Spatial and
Temporal Denoiser (HSTDenoiser) to improve the hierarchical modeling of each
joint. Our HSTDenoiser comprises two components: the Hierarchical-Related
Spatial Transformer (HRST) and the Hierarchical-Related Temporal Transformer
(HRTT). HRST exploits joint spatial information and the influence of the parent
joint on each joint for spatial modeling, while HRTT utilizes information from
both the joint and its hierarchical adjacent joints to explore the hierarchical
temporal correlations among joints.
