Predicting accurate future trajectories of multiple agents is essential for autonomous systems
but is challenging due to the complex interaction between agents and the uncertainty in …

For autonomous vehicles (AVs) to behave appropriately on roads populated by human-
driven vehicles, they must be able to reason about the uncertain intentions and decisions of …

Deep generative models are often used for human motion prediction as they are able to
model multi-modal data distributions and characterize diverse human behavior. While much …

First-person video naturally brings the use of a physical environment to the forefront, since it
shows the camera wearer interacting fluidly in a space based on his intentions. However …

Accurate affordance detection and segmentation with pixel precision is an important piece in
many complex systems based on interactions, such as robots and assitive devices. We …

Neural rendering is fuelling a unification of learning, 3D geometry and video understanding
that has been waiting for more than two decades. Progress, however, is still hampered by a …

Egocentric videos can bring a lot of information about how humans perceive the world and
interact with the environment, which can be beneficial for the analysis of human behaviour …

Modeling multi-modal high-level intent is important for ensuring diversity in trajectory
prediction. Existing approaches explore the discrete nature of human intent before …

Trajectory or behavior prediction of traffic agents is an important component of autonomous
driving and robot planning in general. It can be framed as a probabilistic future sequence …

First-person video highlights a camera-wearer's activities in the context of their persistent
environment. However, current video understanding approaches reason over visual features …