Predicting the future motion of surrounding agents is essential for autonomous vehicles
(AVs) to operate safely in dynamic human-robot-mixed environments. Context information …

The perception of motion behavior in a dynamic environment holds significant importance
for autonomous driving systems wherein class-agnostic motion prediction methods directly …

Trajectory prediction is an important task that involves modeling the indeterminate nature of
agents to forecast future trajectories given the observed trajectory sequences. The task of …

Human pose forecasting garners attention for its diverse applications. However challenges
in modeling the multi-modal nature of human motion and intricate interactions among agents …

Predicting accurate future trajectories of agents is essential for autonomous navigation in
complex scenarios. Although numerous work has made great progress on this goal, it is still …

Human motion prediction is crucial for human-centric multimedia understanding and
interacting. Current methods typically rely on ground truth human poses as observed input …

The inherently diverse and uncertain nature of trajectories poses a formidable challenge in
accurately modelling them. Motion prediction systems must effectively learn spatial and …

Effective decision-making in autonomous driving relies on accurate inference of other traffic
agents' future behaviors. To achieve this, we propose an online belief-update-based …

Most prior motion prediction endeavors in autonomous driving have inadequately encoded
future scenarios, leading to predictions that may fail to accurately capture the diverse …

Accurate prediction the future movements of surrounding traffic participants is crucial for
autonomous driving. Among various strategies, learning complex interactive behaviors …