Future prediction is a fundamental principle of intelligence that helps plan actions and avoid
possible dangers. As the future is uncertain to a large extent, modeling the uncertainty and …

We consider the problem of predicting the future trajectory of scene agents from egocentric
views obtained from a moving platform. This problem is important in a variety of domains …

We present an approach for pixel-level future prediction given an input image of a scene.
We observe that a scene is comprised of distinct entities that undergo motion and present an …

Multivariate time series forecasting is an important yet challenging problem in machine
learning. Most existing approaches only forecast the series value of one future moment …

We present a novel deep learning architecture for probabilistic future prediction from video.
We predict the future semantics, geometry and motion of complex real-world urban scenes …

Temporal prediction is critical for making intelligent and robust decisions in complex
dynamic environments. Motion prediction needs to model the inherently uncertain future …

Abstract Combining Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs)
with Recurrent Neural Networks (RNNs) for spatiotemporal forecasting has yielded …

Abstract We introduce a Deep Stochastic IOC RNN Encoder-decoder framework, DESIRE,
for the task of future predictions of multiple interacting agents in dynamic scenes. DESIRE …

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 …

In self-driving, predicting future in terms of location and motion of all the agents around the
vehicle is a crucial requirement for planning. Recently, a new joint formulation of perception …