  Estimating 3D human poses only from a 2D human pose sequence is thoroughly
explored in recent years. Yet, prior to this, no such work has attempted to
unify 2D and 3D pose representations in the shared feature space. In this
paper, we propose \mpm, a unified 2D-3D human pose representation framework via
masked pose modeling. We treat 2D and 3D poses as two different modalities like
vision and language and build a single-stream transformer-based architecture.
We apply two pretext tasks, which are masked 2D pose modeling, and masked 3D
pose modeling to pre-train our network and use full-supervision to perform
further fine-tuning. A high masking ratio of $71.8~\%$ in total with a
spatio-temporal mask sampling strategy leads to better relation modeling both
in spatial and temporal domains. \mpm~can handle multiple tasks including 3D
human pose estimation, 3D pose estimation from occluded 2D pose, and 3D pose
completion in a \textbf{single} framework. We conduct extensive experiments and
ablation studies on several widely used human pose datasets and achieve
state-of-the-art performance on MPI-INF-3DHP.
