  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.
