We present MocapNET, an ensemble of SNN encoders that estimates the 3D human body pose based on 2D joint estimations extracted from monocular RGB images. MocapNET provides an efficient divide and conquer strategy for supervised learning. It outputs skeletal information directly into the BVH format which can be rendered in real-time or imported without any additional processing in most popular 3D animation software. The proposed architecture achieves 3D human pose estimations at state of the art rates of 400Hz using only CPU processing.
Please cite the following paper if this work helps your research :
@inproceedings{Qammaz2019,
author = {Qammaz, Ammar and Argyros, Antonis A},
title = {MocapNET: Ensemble of SNN Encoders for 3D Human Pose Estimation in RGB Images},
booktitle = {British Machine Vision Conference (BMVC 2019) (to appear)},
publisher = {BMVA},
year = {2019},
month = {September},
address = {Cardiff, UK},
url = {http://users.ics.forth.gr/ argyros/res_mocapnet.html},
projects = {CO4ROBOTS,MINGEI},
pdflink = {http://users.ics.forth.gr/ argyros/mypapers/2019_09_BMVC_mocapnet.pdf},
videolink = {https://youtu.be/fH5e-KMBvM0}
}
MocapNET is a high performance 2D to 3D single person pose estimator. This code base targets recent Linux (Ubuntu) machines, and relies on the Tensorflow C-API and OpenCV.
Tensorflow is used as the Neural Network framework for our work and OpenCV is used to enable the acquisition of images from webcams or video files as well as to provide an easy visualization method.
We have provided an initialization script that automatically handles most dependencies, as well as download all needed pretrained models. After running it the application should be ready for use.
Any issues not automatically resolved by the script can be reported on the issues section of this repository.
In order to enable an easy to use demo with as few dependencies as possible we have included a MocapNETBenchmark utility which has hardcoded input and output that can run even in a system without OpenCV to give you a performance estimation of our method. If you have OpenCV available you can use our live demo ( WebcamJointBIN binary ) that will use the VNect 2D joint estimator automatically downloaded using the initialize.sh script. However in order to achieve higher accuracy estimations you are advised to set up OpenPose and use it to acquire JSON files with 2D detections that can be subsequently converted to 3D BVH files using the MocapNETJSON binary. They will provide superior accuracy compared to the VNect 2D joint detector which is used for faster performance in the live demo since 2D estimation is the bottleneck of the application. Our live demo will try to run the Vnect 2D Joint estimation on your GPU and MocapNET 3D estimation on the system CPU to achieve a combined framerate of over 60fps. Unfortunately there are many GPU compatibility issues with Tensorflow C-API builds since recent versions have dropped CUDA 9.0 support as well as compute capabilities that might be required by your system, you can edit the initialize.sh script and change the variable TENSORFLOW_VERSION according to your needs. If you want CUDA 9.0 you should se it to 1.12.0. If you want CUDA 9.0 and have a card with older compute capabilities (5.2) then choose version 1.11.0. If all else fails you can always recompile the tensorflow C-API to match your specific hardware configuration.
If you are interested in generating BVH training data we have also provided the code that handles randomization and pose perturbation which is accessible using the scripts createRandomizedDataset.sh and createTestDataset.sh. All BVH manipulation code is imported from a secondary github project that is automatically downloaded, included and built using the initialization script. These two scripts will populate the dataset/ directory with csv files that contain valid training samples based on the CMU dataset.
To compile the library issue :
sudo apt-get install build-essential cmake libopencv-dev
./initialize.sh
mkdir build
cd build
cmake ..
make
To test the library performance on the CPU of your computer issue :
./MocapNETBenchmark --cpu
The output should provide you with a model name of your CPU as well as the average framerate for 1000 samples evaluated, as seen in the following screenshot.
To test OpenCV support of your webcam issue :
./WebcamBIN --from /dev/video0
To test OpenCV support of your video files issue :
./WebcamBIN --from /path/to/yourfile.mp4
These tests only use OpenCV (without Tensorflow or any other dependencies) and are intended as a quick test that can help identify and debug configuration problems on your system.
Assuming that the WecamBIN executable is working correctly, to do a live test of the MocapNET library using a webcam issue :
./WebcamJointBIN --from /dev/video0 --live
To dump 5000 frames from the webcam to out.bvh instead of the live directive issue :
./WebcamJointBIN --from /dev/video0 --frames 5000
To test the library using a pre recorded video file issue :
./WebcamJointBIN --from /path/to/yourfile.mp4
The output window of WebcamJointBIN contains a heatmap depicting the 2D Joint estimations, an RGB image cropped and centered on the observed person, a 2D overlay of the 2D Skeleton as well as a window that has the 3D output retrieved by our method as seen in the following image.
BVH output files can be easily viewed using a variety of compatible applicatons. We suggest BVHacker that is free for download and compatible with Wine
In order to get higher accuracy output compared to the live demo which is more performance oriented, you can use OpenPose and the 2D output JSON files produced by it. The MocapNETJSON application will convert them to a BVH file. After getting OpenPose and building it you can generate 2D JSON body pose data by running :
build/examples/openpose/openpose.bin -number_people_max 1 --hand --write_json /path/to/outputJSONDirectory/ -video /path/to/yourVideoFile.mp4
This will create files in the following fashion /path/to/outputJSONDirectory/yourVideoFile_XXXXXXXXXXXX_keypoints.json
You can convert them to a BVH file by issuing :
./MocapNETJSON --from /path/to/outputJSONDirectory/ --label yourVideoFile --seriallength 12 --size 1920 1080
This library is provided under the FORTH license