AdvMT: Adversarial Motion Transformer for Long-term Human Motion Prediction

arXiv:2401.05018v2 Announce Type: replace
Abstract: 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 …

abstract adversarial arxiv collaboration cs.cv data environment future future human human humans long-term movements networks prediction robots transformer type