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.

翻译：合成人体骨骼的时空动态仍然是一项艰巨的任务,不仅从生成的形状的质量来看,而且从其多样性来看,特别是综合某一具体行动(动作调节)的现实体运动(动作调节)而言,这仍然是一项具有挑战性的任务。 在本文中,我们提议“动因-GAN”是一个新型结构,它利用基因反转网络和图象进化网络的惠益,合成人体动能。拟议的对抗性结构可以对地方和全球身体运动采取120种不同行动,同时通过潜伏空间分解和蒸汽变异改善样本质量和多样性。我们的实验是在三个众所周知的数据集中进行的,其中“基尼特-GAN”在分布质量计量方面显然超过了最先进的方法,同时能够合成不同行动数量的一个以上等量。我们的代码和模型可以在https://github.com/DegardinBruno/Kineti-GAN上公开查阅。