  The pedestrian trajectory prediction task is an essential component of
intelligent systems. Its applications include but are not limited to autonomous
driving, robot navigation, and anomaly detection of monitoring systems. Due to
the diversity of motion behaviors and the complex social interactions among
pedestrians, accurately forecasting their future trajectory is challenging.
Existing approaches commonly adopt GANs or CVAEs to generate diverse
trajectories. However, GAN-based methods do not directly model data in a latent
space, which may make them fail to have full support over the underlying data
distribution; CVAE-based methods optimize a lower bound on the log-likelihood
of observations, which may cause the learned distribution to deviate from the
underlying distribution. The above limitations make existing approaches often
generate highly biased or inaccurate trajectories. In this paper, we propose a
novel generative flow based framework with dual graphormer for pedestrian
trajectory prediction (STGlow). Different from previous approaches, our method
can more precisely model the underlying data distribution by optimizing the
exact log-likelihood of motion behaviors. Besides, our method has clear
physical meanings for simulating the evolution of human motion behaviors. The
forward process of the flow gradually degrades complex motion behavior into
simple behavior, while its reverse process represents the evolution of simple
behavior into complex motion behavior. Further, we introduce a dual graphormer
combining with the graph structure to more adequately model the temporal
dependencies and the mutual spatial interactions. Experimental results on
several benchmarks demonstrate that our method achieves much better performance
compared to previous state-of-the-art approaches.
