Papers › PoseLifter: Absolute 3D human pose lifting network from a single noisy 2D human pose

PoseLifter: Absolute 3D human pose lifting network from a single noisy 2D human pose

26 Oct 2019arXiv:1910.12029archive 2025-07-28

Ju Yong Chang, Gyeongsik Moon, Kyoung Mu Lee

This study presents a new network (i.e., PoseLifter) that can lift a 2D human pose to an absolute 3D pose in a camera coordinate system. The proposed network estimates the absolute 3D location of a target subject and generates an improved 3D relative pose estimation compared with existing pose-lifting methods. Using the PoseLifter with a 2D pose estimator in a cascade fashion can estimate a 3D human pose from a single RGB image. In this case, we empirically prove that using realistic 2D poses synthesized with the real error distribution of 2D body joints considerably improves the performance of our PoseLifter. The proposed method is applied to public datasets to achieve state-of-the-art 2D-to-3D pose lifting and 3D human pose estimation.

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juyongchang/PoseLifter officialmentioned in papermentioned on GitHubpytorch report

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Tasks

3D Human Pose EstimationPose Estimation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Human Pose Estimation Human3.6M PoseLifter Average MPJPE (mm) 52.5 #76 of 88 Archive leaderboard report
3D Human Pose Estimation Human3.6M PoseLifter Multi-View or Monocular Monocular #76 of 88 Archive leaderboard report
3D Human Pose Estimation Human3.6M PoseLifter PA-MPJPE 39.1 #76 of 88 Archive leaderboard report
3D Human Pose Estimation Human3.6M PoseLifter Using 2D ground-truth joints No #76 of 88 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP PoseLifter AUC 45.1 #93 of 108 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP PoseLifter PCK 84 #93 of 108 Archive leaderboard report

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