Browse State-of-the-Art › 3D Multi-Person Human Pose Estimation
3D Multi-Person Human Pose Estimation
4 papers with code · 0 benchmarks · 1 dataset archive 2025-07-28
Benchmarks archive 2025-07-28
No benchmark for this task in the archive.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
1 dataset whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
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Most implemented papers archive 2025-07-28
4 shown of 4 papers with code (8 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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1 Jul 2019 4 repositories listedThe first stage is a convolutional neural network (CNN) that estimates 2D and 3D pose features along with identity assignments for all visible joints of all individuals.
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22 Jun 2021 1 repository listedThis paper introduces an approach for multi-human 3D pose estimation and tracking based on calibrated multi-view.
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15 Apr 2021 1 repository listed Syntology ran 3 of 9 samples · 6 unverifiedSecond, we propose a joint-based regressor that distinguishes a target person's feature from others.
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21 Jul 2020 1 repository listed Syntology ran 0 of 19 samples · 19 unverifiedIn this paper, we depart from the multi-person 3D pose estimation formulation, and instead reformulate it as crowd pose estimation.
Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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