ReVA-toolkit

Open-source avatar for real-time human-agent interaction applications.

This is an attempt to respond to the need of the scientific community for an open-source, free of use, efficient virtual agent flexible enough to be used in projects varying from rapid prototyping and system testing to entire HAI applications and experiments.

Installation

This project is using python3 (pre-processing of motion-capture) and godot engine.

Python dependencies

$ pip install numpy
$ pip install pyosc

Godot installation

To edit the avatar, you need to recompile godot engine with an extra module for OSC.

On Linux, first install godot dependencies and run install.sh to install and compile godot. The bash file already contains all the command lines here below. The script will create a forge.godot folder in your user home directory, download, compile and run the godot project.

For Windows & OSX, you will need to:

• download source code of godot from github

$ git clone git@github.com:godotengine/godot.git godot

• download source code of OSC module for godot in modules folder and run install script

$ cd godot/modules/
$ git clone git@gitlab.com:frankiezafe/gosc.git gsoc
$ cd gsoc
$ python install.py

• install dependencies of godot, see official docs
• recompile godot

$ cd ../../
$ scons platform=[your OS: win, x11 or osx]

This will generate an executable in the folder bin. It will be name godot.[your OS].tools.[your OS bites], typically godot.x11.tools.64 for linux.

Now you can download the source of this repository.

$ cd ~
$ cd git clone git@github.com:numediart/ReVA-toolkit.git

Once done, run your local version of godot, import project.godot and run it.

OpenFace Python

To control the avatar in realtime with OpenFace, clone this repository and follow the instructions at the top of the readme.

how to record files for ReVA

After compilation of Openface, launch FeatureExtraction, located in folder build/bin, with these arguments

./FeatureExtraction -2Dfp -3Dfp -pose -gaze -aus -device 0

This will generate a csv in the folder processed next to the application.

Once generated, edit python/openface_json.py in this repository and adapt these variables:

CSV_PATH = [path to the csv generated by FeatureExtraction]
JSON_PATH = [path of the json that will be generated by the script]

For consitency, set the json to godot/assets/json/[name].json. It already contains openface json's.

Repeat these steps for each csv generated.

To load the files in godot, use:

./launch

to open the project in godot. Play the project ( 'play' icon at the top right of the interface ), and use load button in the main panel.

You can keep the application running while you generate files.