Most of the previous 3D human pose estimation work relied on the powerful
memory capability of the network to obtain suitable 2D-3D mappings from the
training data. Few works have studied the modeling of human posture deformation
in motion. In this paper, we propose a new modeling method for human pose
deformations and design an accompanying diffusion-based motion prior. Inspired
by the field of non-rigid structure-from-motion, we divide the task of
reconstructing 3D human skeletons in motion into the estimation of a 3D
reference skeleton, and a frame-by-frame skeleton deformation. A mixed
spatial-temporal NRSfMformer is used to simultaneously estimate the 3D
reference skeleton and the skeleton deformation of each frame from 2D
observations sequence, and then sum them to obtain the pose of each frame.
Subsequently, a loss term based on the diffusion model is used to ensure that
the pipeline learns the correct prior motion knowledge. Finally, we have
evaluated our proposed method on mainstream datasets and obtained superior
results outperforming the state-of-the-art.

本文提出了一种新的人体姿势变形建模方法，并设计了一种基于扩散的运动先验。通过非刚性运动结构重建 3D 人体骨骼的任务分为估计 3D 参考骨架和逐帧骨架变形。同时使用混合时空 NRSfMformer 从 2D 观察序列中估计每一帧的 3D 参考骨架和骨架变形，并将它们相加得到每一帧的姿势。最后，使用基于扩散模型的损失项确保管道学习正确的先验运动知识。最终，在主流数据集上评估了提出的方法，并获得了优于现有技术的结果。