  Synthesising the spatial and temporal dynamics of the human body skeleton
remains a challenging task, not only in terms of the quality of the generated
shapes, but also of their diversity, particularly to synthesise realistic body
movements of a specific action (action conditioning). In this paper, we propose
Kinetic-GAN, a novel architecture that leverages the benefits of Generative
Adversarial Networks and Graph Convolutional Networks to synthesise the
kinetics of the human body. The proposed adversarial architecture can condition
up to 120 different actions over local and global body movements while
improving sample quality and diversity through latent space disentanglement and
stochastic variations. Our experiments were carried out in three well-known
datasets, where Kinetic-GAN notably surpasses the state-of-the-art methods in
terms of distribution quality metrics while having the ability to synthesise
more than one order of magnitude regarding the number of different actions. Our
code and models are publicly available at
https://github.com/DegardinBruno/Kinetic-GAN.
