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TotalCapture

  • Modality: Optical motion capture with synchronized multi-view RGB, depth, IMU
  • Primary Tasks: 3D pose estimation, multi-view reconstruction, IMU-to-mocap fusion
  • Scale: 5 subjects, 4 camera views, 4 Vicon cameras, 11 sequences per subject
  • License: Creative Commons Attribution 4.0 (non-commercial recommended)
  • Access: http://totalcapture.net/

Summary

TotalCapture provides high-quality motion capture data aligned with RGB videos, depth maps, and inertial measurements. It is widely used to evaluate multi-view reconstruction and to benchmark IMU fusion algorithms thanks to precise calibration between sensors and mocap ground truth.

Reference Paper

  • Matthew Trumble et al. "Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies." IEEE TPAMI, 2019. PDF

Benchmarks & Baselines

  • TotalCapture baseline - MPJPE: 19.0 mm for multi-view reconstruction; Trumble et al., 2019.
  • VNect + Fusion - MPJPE: 28.2 mm when fusing IMU data; Mehta et al., SIGGRAPH 2017.
  • Standard evaluation splits include Freestyle, Walking, Acting, and Rom sequences, often with cross-subject validation.

Tooling & Ecosystem

Known Challenges

  • Dataset size is modest; combine with AMASS or Human3.6M for training when possible.
  • Calibration metadata requires careful parsing; check camera intrinsics/extrinsics before reprojection.
  • IMU data contains drift over long sequences; use provided synchronization markers.

Cite

@article{trumble2019totalcapture,
  title   = {Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies},
  author  = {Trumble, Matthew and Gilbert, Andrew and Hilton, Adrian and others},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year    = {2019}
}