Previous probabilistic models for 3D Human Pose Estimation (3DHPE) aimed to enhance pose accuracy by generating multiple hypotheses. However, most of the hypotheses generated deviate substantially from the true pose. Compared to deterministic models, the excessive uncertainty in probabilistic models leads to weaker performance in single-hypothesis prediction. To address these two challenges, we propose a diffusion-based refinement framework called DRPose, which refines the output of deterministic models by reverse diffusion and achieves more suitable multi-hypothesis prediction for the current pose benchmark by multi-step refinement with multiple noises. To this end, we propose a Scalable Graph Convolution Transformer (SGCT) and a Pose Refinement Module (PRM) for denoising and refining. Extensive experiments on Human3.6M and MPI-INF-3DHP datasets demonstrate that our method achieves state-of-the-art performance on both single and multi-hypothesis 3DHPE. Code is available at https://github.com/KHB1698/DRPose.

翻译：先前的三维人体姿态估计（3DHPE）概率模型旨在通过生成多个假设来提升姿态精度。然而，所生成的大多数假设与真实姿态存在显著偏差。与确定性模型相比，概率模型中过高的不确定性导致其在单假设预测中表现较弱。为解决这两个挑战，我们提出了一种基于扩散的细化框架DRPose，该框架通过逆向扩散对确定性模型的输出进行细化，并利用多噪声多步细化实现更适合当前姿态基准的多假设预测。为此，我们提出了一种可扩展图卷积Transformer（SGCT）和一个姿态细化模块（PRM）用于去噪和细化。在Human3.6M和MPI-INF-3DHP数据集上的大量实验表明，我们的方法在单假设和多假设三维人体姿态估计任务中均达到了最先进的性能。代码已开源在https://github.com/KHB1698/DRPose。