  To achieve seamless collaboration between robots and humans in a shared
environment, accurately predicting future human movements is essential. Human
motion prediction has traditionally been approached as a sequence prediction
problem, leveraging historical human motion data to estimate future poses.
Beginning with vanilla recurrent networks, the research community has
investigated a variety of methods for learning human motion dynamics,
encompassing graph-based and generative approaches. Despite these efforts,
achieving accurate long-term predictions continues to be a significant
challenge. In this regard, we present the Adversarial Motion Transformer
(AdvMT), a novel model that integrates a transformer-based motion encoder and a
temporal continuity discriminator. This combination effectively captures
spatial and temporal dependencies simultaneously within frames. With
adversarial training, our method effectively reduces the unwanted artifacts in
predictions, thereby ensuring the learning of more realistic and fluid human
motions. The evaluation results indicate that AdvMT greatly enhances the
accuracy of long-term predictions while also delivering robust short-term
predictions
