Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative Adversarial Neural Networks

Related tags

Deep Learning ForecastingNonverbalSignals
Overview

ForecastingNonverbalSignals

This is the implementation for the paper Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative Adversarial Neural Networks.

Dependencies

  • python 3.6
  • tensorFlow 1.15
  • numpy
  • pickle5
  • sklearn
  • pandas
  • h5py

Usage

  1. Install virtual environment named SocialActionGAN with dependencies:
conda create -n SocialActionGAN python=3.6 tensorflow=1.15 pickle5 scikit-learn pandas h5py
  1. Download UDIVA_2d.pickle, and put it in the folder dataset. Training model with the default parameters:
(SocialActionGAN): python train.py
  1. Alternatively, download the pre-trained model and put it in the folder model. Forecast the motions, generate the ouput file based on the format of the challenge:
(SocialActionGAN): python generate.py --annotations_dir "/path_to/talk_annotations_test_masked/" --segments_path "/path_to/test_segments_topredict.csv"

Optional

  1. Extract the training data, package it as UDIVA_2d.pickle:
(SocialActionGAN): python preprocessing.py --annotations_dir "/path_to/talk_annotations_train"

License

Apache License 2.0

Citation

If you use this repository for your research, please cite:

@misc{tuyen2021forecasting,
      title={Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative Adversarial Neural Networks}, 
      author={Nguyen Tan Viet Tuyen and Oya Celiktutan},
      year={2021},
      eprint={2110.09378},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}
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