AI Dancer

A Long Short Term Memory Neural Network and Style GAN based Dancer that can generate new dance steps.

Explanation video

Final GAN Output

Intermediate LSTM Output

Table of Contents

• AI Dancer
  • Final GAN Output
  • Intermediate LSTM Output
  • Table of Contents
  • Getting Started
• Dancer with LSTM
  • Data Format
  • Adding Training Data
  • Generating new Dance Moves
• Generating new Images with GAN
  • Data Format
  • Training GAN
  • Generating Images
  • Generating Video
• Extras
  • Future Improvements
  • Contributing
  • Author
  • License
  • Acknowledgments

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development.

What things you need to install the software and how to install them

tensorflow==2.1.0
numpy==1.18.4
pandas==1.0.3
joblib==0.16.0

Dancer with LSTM

A step by step series of examples that tell you how to get a trained model

Data Format

The rows of the csv file should contain 26 cols, as the posnet outputs 13 body points along with x , y co-ordinates. exmaple.csv

22.955015543143883,72.49466019717806,27.46597426862512,76.79345393785027,18.440016123751217,76.96221626385844,27.696941055981966,85.40758682459185,17.291410152442737,83.79467011985258,25.599847328825536,89.44064099049706,19.33106420565302,88.3909251787509,25.5050841214364,91.59095023528874,19.353783085099902,91.11421950193409,24.34983123719808,103.17374728018777,18.205918577911792,103.16785845840187,22.1979048879523,112.20190911543995,17.676485117415936,112.5701994124444

Suggested order

"nose",
"leftShoulder",
"rightShoulder",
"leftElbow",
"rightElbow",
"leftWrist",
"rightWrist",
"leftHip",
"rightHip",
"leftKnee",
"rightKnee",
"leftAnkle",
"rightAnkle",

CSV examples are already included in the data/ directory.

Adding Training Data

copy your csv files in the data/ directory, if more than one file is to be added name them {filename}%d.csv.

check train.py

> python train.py

Generating new Dance Moves

When the above steps are done trained models weight will be saved to the weights/ directory, select the weights file with highest accuracy.

check generate.py

> python generate.py {path to weightsfile} {num of steps to generate}

New csv file will be generated new_moves.csv with rows containing position points

Generating new Images with GAN

Data Format

In the next steps I made a visualizer in P5.js to display the csv data in the form of stick figure, then I saved those images as target images for the GAN, the samples are shared in target/ directory.

SAMPLE

In the images/ directory there are images which contain the stick figure as well as the real image which was used to generate the training data

SAMPLE

Training GAN

Copy your training images in the images/ folder, and run the command

!# 10 epochs
> python gan_train.py

the checkpoints will be saved in the training_checkpoints/ directory after every 5 epochs

Generating Images

Copy your target images in the target/ folder, and run the command

> python gan_generate.py

the images will be generated and saved in new_images/ directory.

Generating Video

Now we have our new images generated in new_images/ directory we can convert them into a video using ffmpeg

> ffmpeg -r 1/5 -i new_images/fig%d.jpg -c:v libx264 -vf  "fps=25 ,pad=ceil(iw/2)*2:ceil(ih/2)*2"  -pix_fmt yuv420p out.mp4

this will create out.mp4 file with the images as a video file.

Extras

Future Improvements

1. Improve the GAN and the Neural Network to generate more realistic images
2. Deploying project to Web, to generate new moves on the go.

Contributing

This is a opensource project, if you want to contribute feel free to create a pull request.

Author

Suyash Sonawane - Github | LinkedIn

For any feedback and suggestions mail me at Suyash Sonawane

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

• YouTube video by @carykh