Busting Moves with DanceNet AI

Inspired by STEM-focused YouTuber carykh, Indian developer Jaison Saji has produced a deep network system, DanceNet, that can automatically generate dance moves. Synced used DanceNet to produce a short clip (below) with code published on Github. Interested readers can use the system to improve this work or create their own. DanceNet uses a variational autoencoder (VAE) to automatically generate thousands of single dance pose pictures, then sequentially connect them to produce vigorous dance movements through joint training on Long Short-Term Memory (LSTM) and Mixture Density Networks (MDN). VAE is a commonly used generative model with two parts: an encoder transfers the image into a dense representation that has few dimensions and occupies less space than the original source and stores latent information about the input; while a decoder transfers dense-represented code back to its corresponding image.