Perceiving Systems PS:License 1.0 2018-10-16

Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time

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Perceiving Systems
  • Guest Scientist
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Perceiving Systems
Affiliated Researcher
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Perceiving Systems
Director
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This is the code for our SIGGRAPH Asia 2018 project <Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time>. The BiRNN model training and testing parts along with real-time demo are released to facilitate reproductivity and future research. The large-scale synthetic dataset and real DIP-IMU we introduced in the paper are compatible with this code, and can be accessed via the project page.

Release Date: 16 October 2018
licence_type: PS:License 1.0
Authors: Yinghao Huang and Manuel Kaufmann and Emre Aksan and Michael J. Black and Otmar Hilliges and Gerard Pons-Moll
Link (URL): http://dip.is.tuebingen.mpg.de/
Repository: https://github.com/eth-ait/dip18