'I didn’t want to imitate anybody. Any movement I knew, I didn’t want to use.' – Pina Bausch

I like to work within Conda environments to manage package dependencies. To download Conda (Miniconda is sufficient) for your particular system, and for Python 3, check out: https://docs.conda.io/en/latest/miniconda.html

Once that's installed, clone the repository and set up the Conda environment:

git clone https://github.com/mariel-pettee/choreography.git
cd choreography
conda env create -n choreo -f env_tf1.yml

Then, activate the environment and install a kernel for use in JupyterLab:

conda activate choreo
python -m ipykernel install --user --name choreo --display-name "choreo"

Note that when opening a Jupyter notebook, to use the same packages as you've installed here, you need to select "choreo" from the list of kernels within your notebook.

To display animations live in the Jupyter notebook environment, we recommend installing ffmpeg (https://ffmpeg.org/download.html) in your main repository directory. If you'd prefer not to do this, you can also change the to_html5_video() commands to .to_jshtml().

This model, inspired by chor-rnn (https://arxiv.org/abs/1605.06921), uses 3 LSTM layers to predict new poses given a prompt of a sequence of poses. The length of the prompt is called look_back. We use a Mixture Density Network (https://publications.aston.ac.uk/id/eprint/373/1/NCRG_94_004.pdf) to create multiple Gaussian distributions of potential poses given a prompt sequence. The number of Gaussian distributions is determined by n_mixes.

You can experiment with this model interactively in a Jupyter notebook using rnn.ipynb or via the command line with commands such as:

conda activate choreo
python rnn.py rnn_test --cells 64 64 64 64 --n_mixes 25 --look_back 128 --batch_size 128 --n_epochs 10 --lr 1e-4 --use_pca True

This model uses an autoencoder structure to compress each pose into a lower-dimensional latent space and then back into its original dimension. After sufficient training, the latent space will group similar poses together, and sequences of poses can be visualized as paths throughout the latent space. Users can also construct their own movement sequences by drawing paths throughout the latent space and decoding them into their original dimensions. The interactive Jupyter notebook is pose_autoencoder.ipynb.

This model also uses an autoencoder structure, but for fixed-length sequences of movements, or 'phrases'. This can be then used in two primary ways:

1. Sample randomly from within a given standard deviation in the latent space (which, when well-trained, should resemble an n-dimensional Gaussian distribution) to generate a new fixed-length movement sequence
2. Look at the location of a given sequence in data in the latent space, then add a small deviation to this location and observe its motion. Small deviations (~0.5 sigma or less) will usually closely resemble the original sequence with subtle differences in timing or expressiveness. Larger deviations (~1 sigma or larger) will often capture a similar choreographic idea to the original phrase, but will become increasingly inventive.

Users can experiment with the interactive Jupyter notebook sequence_autoencoder.ipynb.