Speeck-Driven-Facial-Animation

Developed by Fengru Li, Xiaoyi Tang, Fei Tao, Ke Xu

Motivation

Nonverbal behaviour signals, such as facial expressions, provide key information about what we think, act or react. It is an attractive but also challenging to study the signals because they are always hidden or may vary from different people. In this project, we are trying to use machine learning methods for modelling human facial expressions. Therefore, we can get a framework which enables us to predict the facial expression of a never-seen-person when we only hear that person speak.

Technical details

Datasets:

RAVDESS

Framework Architecture:

Our project mainly contains three parts:

feature extraction for audio and video (Fast Fourier Transform(FFT) and landmarks transformation)
mapping from speech to facial feature (CNN + RNN)
face feature translation to images (Deep Convolution Generated Adversarial Networks (DCGAN))

Instruction

Preprocess Dataset:

Put all speech files in ./speech folder
Put train video files in ./train/video folder
Put test video files in ./test/video folder
Use python3 to run preprocess_train.py and preprocess_test.py to get feature array, such as: python3 preprocess_train.py OR python3 preprocess_test.py

Train CNN:

Create folder called CNN_record in the same path(./)
Use python3 to run CNN_train.py, such as: python3 CNN_train.py

Evaluation CNN:

Use python3 to run CNN_eval.py, such as: python3 CNN_train.py

Make Prediction of landmarks:

Creat folder called predict_landmark
Use python3 to run predict.py with arguments which is an speech file endwith .wav , such as: python3 predict.py ./speech/03-01-01-01-01-01-01.wav
The output is some images and a gif files. Images are in predict_landmark folder. GIF file is in the same path called landmarks.gif

DCGAN:

Download video from RAVDESS database.
Put videos under folder /video.
mkdir data/fr.
Run "python prep_gan.py" to extract facial images from videos, the output images will be saved in /data/fr/ folder and landmarks will be saved in ./data/landmark.npy.
Run "python main.py --train --epoch 25" to train GAN network.
Run "python main.py" to generate sample images.

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