  Inspired by the recent advances in generative models, we introduce a human
action generation model in order to generate a consecutive sequence of human
motions to formulate novel actions. We propose a framework of an autoencoder
and a generative adversarial network (GAN) to produce multiple and consecutive
human actions conditioned on the initial state and the given class label. The
proposed model is trained in an end-to-end fashion, where the autoencoder is
jointly trained with the GAN. The model is trained on the NTU RGB+D dataset and
we show that the proposed model can generate different styles of actions.
Moreover, the model can successfully generate a sequence of novel actions given
different action labels as conditions. The conventional human action prediction
and generation models lack those features, which are essential for practical
applications.
