  We propose a real-time system for synthesizing gestures directly from speech.
Our data-driven approach is based on Generative Adversarial Neural Networks to
model the speech-gesture relationship. We utilize the large amount of speaker
video data available online to train our 3D gesture model. Our model generates
speaker-specific gestures by taking consecutive audio input chunks of two
seconds in length. We animate the predicted gestures on a virtual avatar. We
achieve a delay below three seconds between the time of audio input and gesture
animation. Code and videos are available at
https://github.com/mrebol/Gestures-From-Speech

Computer Vision and Pattern Recognition