Capturing high-dimensional social interactions and feasible futures is essential for
predicting trajectories. To address this complex nature, several attempts have been devoted …

Modeling the dynamics of people walking is a problem of long-standing interest in computer
vision. Many previous works involving pedestrian trajectory prediction define a particular set …

Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately
anticipate the behaviors of the surrounding vehicles and pedestrians, for safe and reliable …

Pedestrian trajectory prediction is an essential and challenging task for a variety of real-life
applications such as autonomous driving and robotic motion planning. Besides generating a …

Trajectory prediction is confronted with the dilemma to capture the multi-modal nature of
future dynamics with both diversity and accuracy. In this paper, we propose a distribution …

Multimodal prediction results are essential for trajectory prediction task as there is no single
correct answer for the future. Previous frameworks can be divided into three categories …

Human trajectory forecasting is an inherently multimodal problem. Uncertainty in future
trajectories stems from two sources:(a) sources that are known to the agent but unknown to …

Capturing multimodal natures is essential for stochastic pedestrian trajectory prediction, to
infer a finite set of future trajectories. The inferred trajectories are based on observation …

Forecasting human trajectories in complex dynamic environments plays a critical role in
autonomous vehicles and intelligent robots. Most existing methods learn to predict future …

Pedestrian trajectory prediction is critical in many vision tasks but challenging due to the
multimodality of the future trajectory. Most existing methods predict multimodal trajectories …