Planning an autonomous vehicle's (AV) path in a space shared with pedestrians requires
reasoning about pedestrians' future trajectories. A practical pedestrian trajectory prediction …

We present MotionDiffuser, a diffusion based representation for the joint distribution of future
trajectories over multiple agents. Such representation has several key advantages: first, our …

Demystifying the interactions among multiple agents from their past trajectories is
fundamental to precise and interpretable trajectory prediction. However, previous works only …

To realize trajectory prediction, most previous methods adopt the parameter-based
approach, which encodes all the seen past-future instance pairs into model parameters …

The widespread adoption of commercial autonomous vehicles (AVs) and advanced driver
assistance systems (ADAS) may largely depend on their acceptance by society, for which …

Recent advances in trajectory prediction have shown that explicit reasoning about agents'
intent is important to accurately forecast their motion. However, the current research …

Human motion prediction aims to forecast future poses given a sequence of past 3D
skeletons. While this problem has recently received increasing attention, it has mostly been …

Abstract We propose\textit {JFP}, a Joint Future Prediction model that can learn to generate
accurate and consistent multi-agent future trajectories. For this task, many different methods …

Obtaining accurate and diverse human motion prediction is essential to many industrial
applications, especially robotics and autonomous driving. Recent research has explored …

Predicting the future motion of road agents is a critical task in an autonomous driving
pipeline. In this work, we address the problem of generating a set of scene-level, or joint …