Taras Kucherenko, Dai Hasegawa, Gustav Eje Henter, Naoshi Kaneko, Hedvig Kjellström

This repository contains Keras and Tensorflow based implementation of the speech-driven gesture generation by a neural network which was published at International Conference on Intelligent Virtual Agents (IVA'19) and the extention was published in International Journal of Human-Computer Interaction in 2021.

The project website contains all the information about this project, including video explanation of the method and the paper.

This model has been applied to English dataset.

The demo video as well as the code to run the pre-trained model are online.

• Python 3

# if you have GPU
pip install tensorflow-gpu==1.15.2

# if you don't have GPU
pip install tensorflow==1.15.2

pip install -r requirements.txt

# macos
brew install ffmpeg

# ubuntu
sudo add-apt-repository ppa:jonathonf/ffmpeg-4
sudo apt-get update
sudo apt-get install ffmpeg

We write all the parameters which needs to be specified by a user in the capslock.

• Clone this repository
• Download a dataset from https://www.dropbox.com/sh/j419kp4m8hkt9nd/AAC_pIcS1b_WFBqUp5ofBG1Ia?dl=0
• Create a directory named dataset and put two directories motion/ and speech/ under dataset/

• Put the folder with the dataset in the data_processing directory of this repo: next to the script prepare_data.py
• Run the following command

python data_processing/prepare_data.py DATA_DIR
# DATA_DIR = directory to save data such as 'data/'

Note: DATA_DIR is not a directory where the raw data is stored (the folder with data, "dataset" , has to be stored in the root folder of this repo). DATA_DIR is the directory where the postprocessed data should be saved. After this step you don't need to have "dataset" in the root folder any more. You should use the same DATA_DIR in all the following scripts.

After this command:

• train/ test/ dev/ are created under DATA_DIR/
  • in inputs/ inside each directory, audio(id).wav files are stored
  • in labels/ inside each directory, gesture(id).bvh files are stored
• under DATA_DIR/, three csv files gg-train.csv gg-test.csv gg-dev.csv are created and these files have paths to actual data

python data_processing/create_vector.py DATA_DIR N_CONTEXT
# N_CONTEXT = number of context, in our experiments was set to '60'
# (this means 30 steps backwards and forwards)

Note: if you change the N_CONTEXT value - you need to update it in the train.py script.

(You are likely to get a warning like this "WARNING:root:frame length (5513) is greater than FFT size (512), frame will be truncated. Increase NFFT to avoid." )

As a result of running this script

• numpy binary files X_train.npy, Y_train.npy (vectord dataset) are created under DATA_DIR
• under DATA_DIR/test_inputs/ , test audios, such as X_test_audio1168.npy , are created
• when N_CONTEXT = 60, the audio vector's shape is (num of timesteps, 61, 26)
• gesture vector's shape is（num of timesteps, 384) - 384 = 64joints × (x,y,z positions + x,y,z velocities)

Create a directory to save training checkpoints such as chkpt/ and use it as CHKPT_DIR parameter.

python motion_repr_learning/ae/learn_dataset_encoding.py DATA_DIR -chkpt_dir=CHKPT_DIR -layer1_width=DIM

The optimal dimensionality (DIM) in our experiment was 325

Create DATA_DIR/DIM directory

python motion_repr_learning/ae/encode_dataset.py DATA_DIR -chkpt_dir=CHKPT_DIR -restore=True -pretrain=False -layer1_width=DIM

More information can be found in the folder motion_repr_learning

python train.py MODEL_NAME EPOCHS DATA_DIR N_INPUT ENCODE DIM
# MODEL_NAME = hdf5 file name such as 'model_500ep_posvel_60.hdf5'
# EPOCHS = how many epochs do we want to train the model (recommended - 100)
# DATA_DIR = directory with the data (should be same as above)
# N_INPUT = how many dimension does speech data have (default - 26)
# ENCODE = weather we train on the encoded gestures (using proposed model) or on just on the gestures as their are (using baseline model)
# DIM = how many dimension does encoding have (ignored if you don't encode)

python predict.py MODEL_NAME INPUT_SPEECH_FILE OUTPUT_GESTURE_FILE

# Usage example
python predict.py model.hdf5 data/test_inputs/X_test_audio1168.npy data/test_inputs/predict_1168_20fps.txt

# You need to decode the gestures
python motion_repr_learning/ae/decode.py DATA_DIR ENCODED_PREDICTION_FILE DECODED_GESTURE_FILE -restore=True -pretrain=False -layer1_width=DIM -chkpt_dir=CHKPT_DIR -batch_size=8 

Note: This can be used in a for loop over all the test sequences. Examples are provided in the example_scripts folder of this directory

# The network produces both coordinates and velocity
# So we need to remove velocities
python helpers/remove_velocity.py -g PATH_TO_GESTURES

Use scripts in the evaluation folder of this directory.

Examples are provided in the example_scripts folder of this repository

Use animation server

If you use this code in your research please cite the paper:

@article{kucherenko2021moving,
  title={Moving fast and slow: Analysis of representations and post-processing in speech-driven automatic gesture generation},
  author={Kucherenko, Taras and Hasegawa, Dai and Kaneko, Naoshi and Henter, Gustav Eje and Kjellstr{\"o}m, Hedvig},
  journal={International Journal of Human–Computer Interaction},
  doi={10.1080/10447318.2021.1883883},
  year={2021}
}

If you encounter any problems/bugs/issues please contact me on Github or by emailing me at [email protected] for any bug reports/questions/suggestions. I prefer questions and bug reports on Github as that provides visibility to others who might be encountering same issues or who have the same questions.