Multi-view 3D Human Pose Estimation

Benchmarks archive 2025-07-28

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Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

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Most implemented papers archive 2025-07-28

6 shown of 6 papers with code (18 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.

• DeProPose: Deficiency-Proof 3D Human Pose Estimation via Adaptive Multi-View Fusion
  23 Feb 2025 1 repository listed
  This dataset encompasses a wide range of deficiency scenarios, including noise interference, missing viewpoints, and occlusion challenges.
• Multiple View Geometry Transformers for 3D Human Pose Estimation
  18 Nov 2023 1 repository listed
  In this work, we aim to improve the 3D reasoning ability of Transformers in multi-view 3D human pose estimation.
• Probabilistic Triangulation for Uncalibrated Multi-View 3D Human Pose Estimation
  9 Sep 2023 1 repository listed
  The key idea is to use a probability distribution to model the camera pose and iteratively update the distribution from 2D features instead of using camera pose.
• Real-Time Multi-View 3D Human Pose Estimation using Semantic Feedback to Smart Edge Sensors
  28 Jun 2021 1 repository listed
  We present a novel method for estimation of 3D human poses from a multi-camera setup, employing distributed smart edge sensors coupled with a backend through a semantic feedback loop.
• Learnable Triangulation of Human Pose
  14 May 2019 1 repository listed Syntology ran 1 of 17 samples · 16 unverified
  We present two novel solutions for multi-view 3D human pose estimation based on new learnable triangulation methods that combine 3D information from multiple 2D views.
• A Multi-view RGB-D Approach for Human Pose Estimation in Operating Rooms
  25 Jan 2017 1 repository listed
  In this paper, we propose an approach for multi-view 3D human pose estimation from RGB-D images and demonstrate the benefits of using the additional depth channel for pose refinement beyond its use for the generation of…

Syntology lines on 1 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.