A long-standing goal in computer vision is to capture, model, and realistically synthesize
human behavior. Specifically, by learning from data, our goal is to enable virtual humans to …

We present a novel approach for traffic forecasting in urban traffic scenarios using a
combination of spectral graph analysis and deep learning. We predict both the low-level …

Simulation has the potential to massively scale evaluation of self-driving systems, enabling
rapid development as well as safe deployment. Bridging the gap between simulation and …

As autonomous driving systems mature, motion forecasting has received increasing
attention as a critical requirement for planning. Of particular importance are interactive …

This study explores the application of self-supervised learning (SSL) to the task of motion
forecasting, an area that has not yet been extensively investigated despite the widespread …

We present a new algorithm for predicting the near-term trajectories of road agents in dense
traffic videos. Our approach is designed for heterogeneous traffic, where the road agents …

Human motion prediction, ie, forecasting future body poses given observed pose sequence,
has typically been tackled with recurrent neural networks (RNNs). However, as evidenced …

Accurate long-term trajectory prediction in complex scenes, where multiple agents (eg,
pedestrians or vehicles) interact with each other and the environment while attempting to …

Human behavior has the nature of indeterminacy, which requires the pedestrian trajectory
prediction system to model the multi-modality of future motion states. Unlike existing …

In this paper, we tackle the task of scene-aware 3D human motion forecasting, which
consists of predicting future human poses given a 3D scene and a past human motion. A key …