Continuous diffusion models have demonstrated their effectiveness in addressing the inherent uncertainty and indeterminacy in monocular 3D human pose estimation (HPE). Despite their strengths, the need for large search spaces and the corresponding demand for substantial training data make these models prone to generating biomechanically unrealistic poses. This challenge is particularly noticeable in occlusion scenarios, where the complexity of inferring 3D structures from 2D images intensifies. In response to these limitations, we introduce the Discrete Diffusion Pose ($\text{Di}^2\text{Pose}$), a novel framework designed for occluded 3D HPE that capitalizes on the benefits of a discrete diffusion model. Specifically, $\text{Di}^2\text{Pose}$ employs a two-stage process: it first converts 3D poses into a discrete representation through a \emph{pose quantization step}, which is subsequently modeled in latent space through a \emph{discrete diffusion process}. This methodological innovation restrictively confines the search space towards physically viable configurations and enhances the model's capability to comprehend how occlusions affect human pose within the latent space. Extensive evaluations conducted on various benchmarks (e.g., Human3.6M, 3DPW, and 3DPW-Occ) have demonstrated its effectiveness.

连续扩散模型在解决单目三维人体姿势估计中的固有不确定性和不确定性方面表现出有效性，但需要大型搜索空间和大量训练数据，易于生成生物力学上不真实的姿势。为应对这些限制，我们引入了离散扩散姿势（Di2Pose），一种专为遮挡的三维人体姿势估计设计的新型框架，利用离散扩散模型的优势。该方法创新地将三维姿势通过姿势量化步骤转换为离散表示，然后通过离散扩散过程在潜在空间中对其进行建模，将搜索空间限制在物理上可行的配置范围内，并提升了模型理解遮挡对人体姿势的影响的能力。在多个基准数据集上进行的广泛评估（如Human3.6M、3DPW和3DPW-Occ）表明其有效性。

二次姿势：离散扩散模型用于遮挡人体三维姿势估计