TransCGan-based human motion generator

Abstract

The synthesis of human movement is used in a wide range of applications, such as in military, gaming, sports, medical, robotics and many other fields. The current common approach is based on the acquisition of human motion data by motion capture devices. However, employing this method to gather information on human movements is expensive, time-consuming, and subject to space constraints. In order to avoid these problems, our goal is to create a system that can generate a variety of naturalistic movements quickly and inexpensively. We assume that the creation of human motion is a complicated, non-linear process that is amenable to modeling with deep neural networks. First, using optical motion capture equipment, we collect a range of human motion data, which we then pre-process and annotate. After that-we combine Transformer with Conditional GAN (Cgan) to train this human motion generation model with the collected data. Finally, we evaluate this model by qualitative and qualitative means, which can generate multiple human motions from a high-dimensional potential space based on specified labels.