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

Code

This repository contains the code published alongside with our SIGGRAPH Asia paper. It is organised as follows: train_and_eval contains code to train and evaluate the neural networks proposed in the paper. live_demo contains Unity and Python scripts to use the models for real-time inference. data_synthesis contains a script to produce synthetic IMU measurements from SMPL sequences. Please refer to the READMEs in the respective subfolder for more details.

Data

To download the data please visit the project page.

Contact Information

For questions or problems please file an issue or contact manuel.kaufmann@inf.ethz.ch or yinghao.huang@tuebingen.mpg.de.

Citation

If you use this code or data for your own work, please use the following citation:

@article{DIP:SIGGRAPHAsia:2018,
	title = {Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time},
    	author = {Huang, Yinghao and Kaufmann, Manuel and Aksan, Emre and Black, Michael J. and Hilliges, Otmar and Pons-Moll, Gerard},
    	journal = {ACM Transactions on Graphics, (Proc. SIGGRAPH Asia)},
    	volume = {37},
    	pages = {185:1-185:15},
    	publisher = {ACM},
    	month = nov,
    	year = {2018},
    	note = {First two authors contributed equally},
    	month_numeric = {11}
}

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