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

2018-10-16


This is the code for our SIGGRAPH Asia 2018 project . 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.

Author(s): Yinghao Huang and Manuel Kaufmann and Emre Aksan and Michael J. Black and Otmar Hilliges and Gerard Pons-Moll
Department(s): Perceiving Systems
Publication(s): Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time
Authors: Yinghao Huang and Manuel Kaufmann and Emre Aksan and Michael J. Black and Otmar Hilliges and Gerard Pons-Moll
Release Date: 2018-10-16
Repository: https://github.com/eth-ait/dip18
External Link: http://dip.is.tuebingen.mpg.de/