pose-regression - Human pose regression from RGB images
This software implements a human pose regression method based on the Soft-argmax approach, as described in the following paper:
Human Pose Regression by Combining Indirect Part Detection and Contextual Information (link)
Dependencies
The network is implemented using Keras of top of TensorFlow and Python 3.
We provide a code for live demonstration using video frames captured by a webcan. Small changes in the code may be required for hardware compatibility.
The software requires the following packges:
- numpy
- scipy
- keras (2.0 or higher)
- tensorflow (with GPU is better, but is not required)
- pygame (1.9 or higher, only for demonstration)
- matplotlib (only for demonstration)
Citing
If any part of this source code or the pre-trained weights are useful for you, please cite the paper:
@article{Luvizon_CoRR_2017,
author = {Diogo C. Luvizon and Hedi Tabia and David Picard},
title = {{Human Pose Regression by Combining Indirect Part Detection and Contextual Information}},
journal = {CoRR},
volume = {abs/1710.02322},
year = {2017},
url = {http://arxiv.org/abs/1710.02322},
archivePrefix = {arXiv},
eprint = {1710.02322},
timestamp = {Wed, 01 Nov 2017 19:05:43 +0100},
biburl = {http://dblp.org/rec/bib/journals/corr/abs-1710-02322},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
License
The source code and the weights are given under the MIT License.