The following tutorial introduces a system entitled “Granular Dance” that can generate synthetic sequences of dance poses of arbitrary length. This system combines an adversarial autoencoder with a method for sequence concatenation. The autoencoder model uses a combination of conventional neural network (ANN) and long short term memory (LSTM) layers to create short pose sequences. The concatenation method blends joint rotations to create smooth transitions between these short pose sequences. The article focuses on the explanation of the blending mechanism and provides some examples how the latent space of pose sequence encodings can be navigated to discover potentially interesting synthetic pose sequences. <\/p>\n\n\n\n
This tutorial forms part of a series of tutorials on using PyTorch to create and train generative deep learning models. The code for these tutorials is available here<\/a>.<\/p>\n\n\n\n After 400 epochs of training, the pose sequences reconstructed by the autoencoder look like this when rendered as skeleton animation.<\/p>\n\n\n\n