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Estimating 3D human poses only from a 2D human pose sequence is thoroughlyexplored in recent years. Yet, prior to this, no such work has attempted tounify 2D and 3D pose representations in the shared feature space. In thispaper, we propose MPM, a unified 2D-3D human pose representation framework viamasked pose modeling. We treat 2D and 3D poses as two different modalities likevision and language and build a single-stream transformer-based architecture.We apply three pretext tasks, which are masked 2D pose modeling, masked 3D posemodeling, and masked 2D pose lifting to pre-train our network and usefull-supervision to perform further fine-tuning. A high masking ratio of 72.5%in total with a spatio-temporal mask sampling strategy leading to betterrelation modeling both in spatial and temporal domains. MPM can handle multipletasks including 3D human pose estimation, 3D pose estimation from occluded 2Dpose, and 3D pose completion in a single framework. We conduct extensiveexperiments and ablation studies on several widely used human pose datasets andachieve state-of-the-art performance on Human3.6M and MPI-INF-3DHP. Codes andmodel checkpoints are available at https://github.com/vvirgooo2/MPM

MPM: A Unified 2D-3D Human Pose Representation via Masked Pose Modeling