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Human3.6M

  • Modality: Optical motion capture (3D joint positions) + high-resolution RGB + depth + ground-truth camera calibration
  • Primary Tasks: 3D human pose estimation, pose forecasting, mocap to image alignment
  • Scale: 3.6 million 3D frames, 11 professional actors, 15 activity scenarios, 4 synchronized cameras
  • License: Research-only; requires signed license agreement with the Human3.6M consortium
  • Access: http://vision.imar.ro/human3.6m/description.php

Summary

Human3.6M is a cornerstone benchmark for monocular and multi-view 3D pose estimation. Captured with Vicon mocap and synchronized video, it provides high-quality ground-truth joint trajectories aligned to RGB frames, enabling supervised training and precise evaluation of 3D human reconstruction methods.

Reference Paper

  • Catalin Ionescu et al. "Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments." IEEE TPAMI, 2014. PDF

Benchmarks & Baselines

  • Martinez et al. (Simple Baseline) - MPJPE 67.5 mm (procrustes-aligned) on Protocol #2.
  • PoseFormer - MPJPE 44.3 mm on Protocol #1; Zheng et al., ICCV 2021.
  • Evaluation protocols commonly reported: Protocol #1 (17 joints, MPJPE), Protocol #2 (Procrustes-aligned MPJPE), Protocol #3 (prediction on unnormalized coordinates).

Tooling & Ecosystem

  • Official toolkit with MATLAB evaluation scripts available upon request.
  • Human3.6M Toolkit for data preprocessing and baseline models.
  • MMPose maintains configs and evaluation utilities.

Known Challenges

  • Data access approval may take several days; download links can be slow.
  • Mixed lighting conditions and motion blur challenge monocular models.
  • Protocol confusion is common-explicitly document which evaluation protocol you follow.

Cite

@article{ionescu2014human3,
  title   = {Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments},
  author  = {Ionescu, Catalin and Papava, Dragos and Olaru, Vlad and Sminchisescu, Cristian},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year    = {2014}
}